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<journal-id journal-id-type="publisher-id">Front. Public Health</journal-id>
<journal-title>Frontiers in Public Health</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Public Health</abbrev-journal-title>
<issn pub-type="epub">2296-2565</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-id pub-id-type="doi">10.3389/fpubh.2025.1599702</article-id>
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<subj-group subj-group-type="heading">
<subject>Public Health</subject>
<subj-group>
<subject>Original Research</subject>
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<title-group>
<article-title>Environmental surveillance of the health risk of PM<sub>2.5</sub>-bound metals and metalloids in Wuxi, China, from 2020 to 2023</article-title>
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<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Chen</surname>
<given-names>Lijun</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Zhang</surname>
<given-names>Xuhui</given-names>
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<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Zhou</surname>
<given-names>Xiaoxin</given-names>
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<contrib contrib-type="author">
<name>
<surname>Gong</surname>
<given-names>Yan</given-names>
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<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Kong</surname>
<given-names>Lingcan</given-names>
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<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Yukang</given-names>
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<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Wenwei</given-names>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhu</surname>
<given-names>Pengfei</given-names>
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<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention</institution>, <addr-line>Wuxi</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Research Base for Environment and Health in Wuxi, Chinese Center for Disease Control and Prevention</institution>, <addr-line>Wuxi</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0002">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2772829/overview">Tahmeena Khan</ext-link>, Integral University, India</p>
</fn>
<fn fn-type="edited-by" id="fn0003">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1679666/overview">Worradorn Phairuang</ext-link>, Chiang Mai University, Thailand</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1858359/overview">Maja &#x0110;oli&#x0107;</ext-link>, University of Belgrade, Serbia</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Pengfei Zhu, <email>wxcdczpf@163.com</email></corresp>
<fn fn-type="equal" id="fn0001"><p><sup>&#x2020;</sup>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>08</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>13</volume>
<elocation-id>1599702</elocation-id>
<history>
<date date-type="received">
<day>15</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>22</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Chen, Zhang, Zhou, Gong, Kong, Wu, Liu and Zhu.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Chen, Zhang, Zhou, Gong, Kong, Wu, Liu and Zhu</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>PM<sub>2.5</sub> has been a major public concern due to its association with various diseases; however, its contamination is still not well controlled. From 2020 to 2023, the pollution characteristics of PM<sub>2.5</sub>-bound metals and metalloids were monitored in Wuxi, China. The surveillance targeted 26 components, including antimony (Sb), aluminum (Al), arsenic (As), beryllium (Be), cadmium (Cd), chromium (Cr), mercury (Hg), lead (Pb), manganese (Mn), nickel (Ni), selenium (Se), thallium (Tl), barium (Ba), cobalt (Co), copper (Cu), iron (Fe), molybdenum (Mo), silver (Ag), thorium (Th), vanadium (V), zinc (Zn), strontium (Sr), tin (Sn), lithium (Li), uranium (U), and rubidium (Rb). During the study period, The PM<sub>2.5</sub> mass concentration ranged from 5.00 to 166.0&#x202F;&#x03BC;g/m<sup>3</sup>, and the annual average PM<sub>2.5</sub> concentration was 40.4&#x202F;&#x00B1;&#x202F;26.1&#x202F;&#x03BC;g/m<sup>3</sup>. The total concentration of 22 elements was 659.7&#x202F;&#x00B1;&#x202F;318.5&#x202F;ng/m<sup>3</sup>. Fe, Al, Zn, Mn, Pb, Cu, and Ba were seven dominant metals in PM<sub>2.5</sub> accounted for 95.7% of the total metal concentrations (TMs). Both PM<sub>2.5</sub> and most PM<sub>2.5</sub>-bound metals and metalloids exhibited decreasing trends to varying degrees and seasonal characteristics, peaking in winter. The result of enrichment factor (EF) suggested most elements mainly derived from anthropogenic pollution, while industrial emissions (32.4%), automotive emissions (27.9%), fuel combustion (26.2%) and dust emissions (13.5%) identified as the main sources by the positive matrix factorization (PMF). The hazard quotients (HQs) of all the metals were below 1, with Mn exhibiting highest HQ at 6.29&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>&#x202F;&#x00B1;&#x202F;3.28&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>. The carcinogenic risks of five elements were as follows: Cd (5.21&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>&#x202F;&#x00B1;&#x202F;4.02&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>), As (7.00&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>&#x202F;&#x00B1;&#x202F;3.83&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>), Pb (1.24&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>&#x202F;&#x00B1;&#x202F;7.79&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;8</sup>), Ni (3.21&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>&#x202F;&#x00B1;&#x202F;1.62&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>) and Cr (VI) (2.76&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>&#x202F;&#x00B1;&#x202F;1.31&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>). These results indicate that both the non-carcinogenic and carcinogenic risks of individual elements monitored were within an acceptable range. However, considerable attention should be given to the comprehensive exposure risk associated with long-term exposure to Mn, As and Cr (VI). This study updated air pollution data, analyzed pollution sources and characteristics and discussed the potential risks of PM<sub>2.5</sub>-bound metals and metalloids. It is of great significance to reduce PM<sub>2.5</sub> emissions and formulate environmental protection policies to protect the health of local residents.</p>
</abstract>
<kwd-group>
<kwd>air pollution</kwd>
<kwd>PM<sub>2.5</sub></kwd>
<kwd>metal</kwd>
<kwd>source apportionment</kwd>
<kwd>health risk</kwd>
</kwd-group>
<contract-num rid="cn1">22075106</contract-num>
<contract-num rid="cn2">LCZX2021006</contract-num>
<contract-num rid="cn2">CXTD2021004</contract-num>
<contract-num rid="cn3">OHIC2023Z03</contract-num>
<contract-sponsor id="cn1">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content></contract-sponsor>
<contract-sponsor id="cn2">Medical Key Discipline Program of Wuxi Health Commission</contract-sponsor>
<contract-sponsor id="cn3">Open Fund Project of Hubei Provincial Key Laboratory for Occupational Hazard Identification and Control in 2023</contract-sponsor>
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<meta-value>Environmental Health and Exposome</meta-value>
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</front>
<body>
<sec sec-type="intro" id="sec1">
<title>Introduction</title>
<p>Particulate matter (PM) pollution is a significant issue in atmospheric environmental governance, negatively impacting air quality, visibility, climate change, atmospheric radiation, and human health (<xref ref-type="bibr" rid="ref1">1</xref>). In recent years, it has garnered widespread attention in the field of atmospheric science (<xref ref-type="bibr" rid="ref2 ref3 ref4">2&#x2013;4</xref>). PM<sub>2.5</sub>, defined as particulate matter with an aerodynamic equivalent diameter of 2.5&#x202F;&#x03BC;m or less, has a larger specific surface area, allowing it to effectively accumulate organic compounds, viruses, bacteria, metals and metalloids (<xref ref-type="bibr" rid="ref5">5</xref>). Over the past decades, a significant amount of epidemiological evidence has indicated PM<sub>2.5</sub> is associated with diseases. There is a positive correlation between short-term exposure to PM<sub>2.5</sub> and cardiovascular and respiratory diseases (<xref ref-type="bibr" rid="ref6">6</xref>), while long-term exposure is significantly correlated with increased mortality from diabetes, cardiovascular disease, lung cancer, and chronic obstructive pulmonary disease (<xref ref-type="bibr" rid="ref7 ref8 ref9 ref10">7&#x2013;10</xref>). A study from Thailand found that if PM<sub>2.5</sub> concentrations reached the World Health Organization&#x2019;s (WHO) short-term gold standard of 15&#x202F;&#x03BC;g/m<sup>3</sup>, approximately 8 premature deaths per 100,000 people could be prevented. Moreover, if PM<sub>2.5</sub> reached the WHO&#x2019;s long-term gold standard of 5&#x202F;&#x03BC;g/m<sup>3</sup>, an estimated 159 premature deaths per 100,000 population could be avoided (<xref ref-type="bibr" rid="ref11">11</xref>). Furthermore, an <italic>in vivo</italic> experiment in mice confirmed that lung metabolite levels were perturbed after PM<sub>2.5</sub> intratracheal instillation, mainly affecting metabolic pathways such as citric the acid cycle, pyruvate metabolism, purine and pyrimidine metabolism, as well as valine, leucine, and isoleucine metabolism.</p>
<p>In addition, PM<sub>2.5</sub> exposure affected the relative abundance of <italic>Ruminococcaceae</italic>, <italic>Enterobacteriaceae,</italic> and <italic>Pseudomonas</italic> (<xref ref-type="bibr" rid="ref12">12</xref>). Moreover, Tong-Hong Wang et al. (<xref ref-type="bibr" rid="ref13">13</xref>) found that short-term (24-h) exposure to PM<sub>2.5</sub> could activate the EGFR pathway in lung cancer cells, while long-term (90-day) exposure promoted tumor progression through the activation of EGFR and AhR, by enhancing the TMPRSS2-IL18 pathway. Metals and metalloids, as the important components of PM<sub>2.5</sub>, through inhalation, skin exposure, and oral exposure. They are widespread public concern due to their high toxicity, persistence, bioaccumulation and potential health risks to ecosystems and humans (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref15">15</xref>). Research has shown that Long-term exposure to high concentrations of metals and metalloids may lead to cardiotoxicity, neurotoxicity, immunotoxicity, and cancer, thereby increasing mortality rates (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref17">17</xref>).</p>
<p>There is evidence suggesting a close relationship between metal accumulation (such as Zn, Mn, Cu and Ni), abnormal protein expression, and the pathogenesis of neurodegenerative diseases (<xref ref-type="bibr" rid="ref18">18</xref>). Additionally, Baoman Li et al. (<xref ref-type="bibr" rid="ref19">19</xref>) found that excessive intake of metals and metalloids (Such as Hg, Pb, As, Al, Bi, Zn, Cu, Cd, Ni, Mn, and Fe) could disrupt the homeostasis and neuroprotective functions of astrocytes, including the glutamate/GABA-glutamine shuttle, antioxidant mechanisms, and energy metabolism, ultimately leading to neuronal damage and initiating neurodegenerative diseases. The skeletons serve as long-term storage site for lead and other metals, accounting for approximately 90% of the total body burden in mammals. Studies have shown that metal exposure (especially as Hg, Pb, Cd, Cr, Al, and Ni) increases the risk of osteoporosis and fractures (<xref ref-type="bibr" rid="ref20">20</xref>). Moreover, toxic metal ions could exert harmful effects by strongly interacting with essential biomolecules, replacing vital metal ions in proteins, enzymes, or hard structures such as bones and teeth. Additionally, metals with redox properties are key inducers of reactive oxygen species, which may lead to oxidative stress and cellular damage (<xref ref-type="bibr" rid="ref21">21</xref>).</p>
<p>Although metals and metalloids account for less than 10% of PM<sub>2.5</sub>, it is crucial to analyze their pollution characteristics and the impacts on human health due to their toxicity and enrichment effects. In recent years, many scholars have focused on assessing the health risks of metals in PM<sub>2.5.</sub> Data from Kolkata illustrated that concentrations of carcinogenic metals, such as Ni, Pb, Cd, and Cr (VI), exceeded the guideline limits set by U. S. Environmental Protection Agency (USEPA) (<xref ref-type="bibr" rid="ref22">22</xref>). A study conducted in a smelting district in Northeast China found that Cd and Pb in PM<sub>2.5</sub> posed non-carcinogenic health risks, while Cd also presented a potential carcinogenic risk to human health (<xref ref-type="bibr" rid="ref23">23</xref>).</p>
<p>Literature showed that PM<sub>10</sub> may originate from resuspended soil and road dust, construction, crystalline sea salt, etc. PM<sub>0.1</sub> and PM<sub>2.5</sub> are direct emissions from human activities such as open burning, fuel burning, wildfire smoke, as well as secondary formation of volatile organic compounds, nitrogen oxides, and sulfur oxide pollutants (<xref ref-type="bibr" rid="ref24">24</xref>). Phairuang et al. (<xref ref-type="bibr" rid="ref25">25</xref>) found that the primary sources of PM<sub>0.1</sub> were derived from road traffic, industrial sector, and biomass combustion, in Bangkok, Thailand. The chemical composition and risk assessment of PM<sub>2.5</sub> were investigated in the brick kiln site and roadside by Ahmad et al. results indicated the combustion sources were the main source of PM<sub>2.5</sub> (<xref ref-type="bibr" rid="ref26">26</xref>). In order to reduce the health risks caused by PM2.5, it is necessary to monitor the pollution situation, assess potential release sources and risks, as to take measures to control pollution emissions and protect the health of the population.</p>
<p>Wuxi, a water-bound city located in the lower reaches of the Yangtze River in China, had a permanent residential population of 7.49 million and a Gross Domestic Product (GDP) of 1.55 trillion RMB by the end of 2023. In terms of per capita GDP, Wuxi ranked third in China in 2023. As an economically and socially developed city, Wuxi faces environmental challenges that have triggered a series of health impacts (<xref ref-type="bibr" rid="ref27 ref28 ref29 ref30 ref31">27&#x2013;31</xref>). A study demonstrated that As and Sb in the drinking water represented an increasing risk to human health (<xref ref-type="bibr" rid="ref27">27</xref>). Zhu et al. (<xref ref-type="bibr" rid="ref28">28</xref>) found that cancer risk from the cumulative cancer risk trihalomethanes (THMs) exposure in the drinking water was 1.26&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;4</sup> while the non-cancer risk was 2.02&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>. Additionally, evidence showed that prenatal exposure increased the risk of preterm birth and reduced gestational age in Wuxi (<xref ref-type="bibr" rid="ref29">29</xref>). As is well known, differences in climate, geographical conditions, socio-economic structure, and lifestyle can result in varying degrees of environmental pollution. Therefore, long-term monitoring and analysis of PM<sub>2.5</sub> in Wuxi are of great significance.</p>
<p>In Wuxi, there have been few studies on the characteristics, sources, and health risk assessments of PM<sub>2.5</sub>, particularly concerning metals and metalloids in the atmosphere. Wu Keqin et al. (<xref ref-type="bibr" rid="ref32">32</xref>) studied metals in PM<sub>2.5</sub> from 2016 to 2020, but their study had limited elements and neglected the sources of metal elements. This article comprehensively monitors the pollution status of PM<sub>2.5</sub>, analyzing the sources and potential health risks of 26 metals and metalloids in PM<sub>2.5</sub> from 2020 to 2023, including Sb, Al, As, Be, Cd, Cr, Hg, Pb, Mn, Ni, Se, Tl, Ba, Co, Cu, Fe, Mo, Ag, Th, V, Zn, Sr., Sn, Li, U, and Rb. The aim is to provides strategies for air pollution prevention and the protection of public health.</p>
</sec>
<sec sec-type="materials|methods" id="sec2">
<title>Materials and methods</title>
<sec id="sec3">
<title>Sample collection</title>
<p>The sampling site was located on the roof of a primary school in Wuxi City, away from the main roads and without nearby pollution sources. The flow rate of the sampler was 100&#x202F;L/min. Sampling was conducted from January 2020 to December 2023, with samples collected from the 10th to the 16th of each month, or during hazy weather conditions (AQI&#x202F;&#x003E;&#x202F;200). In cases of severe pollution, two filter membranes were employed per day, and the samples were stored at &#x2212;20&#x00B0;C for subsequent analysis. Meteorological parameters, including atmospheric pressure, temperature, relative humidity, precipitation, and wind speed, were recorded at the time of sample collection.</p>
</sec>
<sec id="sec4">
<title>Sample preparation and detection</title>
<p>The PM<sub>2.5</sub> samples were pretreated in accordance with the guidelines outlined in the Handbook for Monitoring and Protecting the Health Effects of Air Pollution on People, released by the China Center for Disease Control and Prevention. The filters membranes were accurately weighed, placed into 15&#x202F;mL centrifuge tubes, and 10&#x202F;mL of a 5% nitric acid solution was added. The samples were immersed in a 70&#x00B0;C-water bath and subjected to ultrasonication for 3&#x202F;h. Following this, the samples were thoroughly shaken and centrifuged at 4500&#x202F;rpm for 5&#x202F;min and filtered through a 0.45&#x202F;&#x03BC;m membrane. The concentrations of 26 metals and metalloids were determined using inductively coupled plasma tandem mass spectrometry (ICP-MS/MS, Agilent Technologies 8,900). Before analysis, Quality calibration and resolution verification on the tuning solution of the mass spectrometer was performed. The tuning solution of the mass spectrometer should be measured at least 4 times, and the relative standard deviation of the signal intensity of the elements contained in the measured tuning solution should be confirmed to be &#x2264; 5%. Under optimized instrument conditions, the reagent blank, standard curve series, sample solution, and quality control sample solution are measured sequentially. The results showed that the concentration measurements of calibration blank, reagent blank, and filter membrane blank for all elements were below the detection limit. The results of certified reference materials are within the scope of the certificate and controllable. The relative standard deviation of the parallel measurement results for all detected elements is less than 8.5%. Then the element recovery percentage from the standard reference material was between 85 and 113%.</p>
</sec>
<sec id="sec5">
<title>Enrichment factor</title>
<p>The enrichment factor (EF), a unitless index was used to differentiate the sources of metals and metalloids in PM<sub>2.5</sub> in Wuxi City. EF values greater than 10 (EF &#x003E; 10) indicate pollution from anthropogenic sources, values less than or equal to 1(EF&#x202F;&#x2264;&#x202F;1) indicate natural sources, and values between 1 and 10 (1&#x202F;&#x003C;&#x202F;EF&#x202F;&#x2264;&#x202F;10) imply a combination of both sources (<xref ref-type="bibr" rid="ref33">33</xref>, <xref ref-type="bibr" rid="ref34">34</xref>). In this study, Al, an element due to its high concentration, good stability in soil, and widespread presence in PM<sub>2.5</sub>, was selected as the reference element. The EF was calculated by applying the following equation (<xref ref-type="disp-formula" rid="EQ1">Equation 1</xref>).</p>
<disp-formula id="EQ1">
<label>(1)</label>
<mml:math id="M1">
<mml:msub>
<mml:mi>EF</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mo stretchy="true">(</mml:mo>
<mml:mfrac>
<mml:msub>
<mml:mi mathvariant="normal">C</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:msub>
<mml:mi mathvariant="normal">C</mml:mi>
<mml:mi>Al</mml:mi>
</mml:msub>
</mml:mfrac>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mo stretchy="true">(</mml:mo>
<mml:mfrac>
<mml:msub>
<mml:mi mathvariant="normal">B</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:msub>
<mml:mi mathvariant="normal">B</mml:mi>
<mml:mi>Al</mml:mi>
</mml:msub>
</mml:mfrac>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
</mml:mfrac>
</mml:math>
</disp-formula>
<p>Where Ci represents the concentration of each heavy metal in PM<sub>2.5</sub> (ng/m<sup>3</sup>), C<sub>Al</sub> represents the concentration of Al in PM<sub>2.5</sub> (ng/m<sup>3</sup>), Bi represents the concentration of each heavy metal in the soil (mg/kg), and B<sub>Al</sub> represents the concentration of Al in the soil. In this study, the background values of soil elements were collected from Nanjing, China (<xref ref-type="bibr" rid="ref35">35</xref>).</p>
</sec>
<sec id="sec6">
<title>PMF analysis</title>
<p>PMF is widely used for source apportionment of atmospheric particulate matter (<xref ref-type="bibr" rid="ref36">36</xref>). This model is a multivariate factor analysis method that identifies and quantifies pollution sources by analyzing the temporal variation in different components. The underlying principle of the PMF can be found in the literature (<xref ref-type="bibr" rid="ref37">37</xref>). In this research, source apportionment of PM2.5-bound elements in the atmosphere of Wuxi was conducted using PMF 5.0. The calculation of data uncertainty (Unc) is as follows:</p>
<disp-formula id="EQ2">
<label>(2)</label>
<mml:math id="M2">
<mml:mi>Unc</mml:mi>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mn>5</mml:mn>
<mml:mn>6</mml:mn>
</mml:mfrac>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>MDL</mml:mi>
</mml:math>
</disp-formula>
<disp-formula id="EQ3">
<label>(3)</label>
<mml:math id="M3">
<mml:mi>Unc</mml:mi>
<mml:mo>=</mml:mo>
<mml:msqrt>
<mml:mrow>
<mml:msup>
<mml:mrow>
<mml:mo stretchy="true">(</mml:mo>
<mml:mi mathvariant="italic">Er</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:mtext mathvariant="italic">Conc</mml:mtext>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msup>
<mml:mo>+</mml:mo>
<mml:msup>
<mml:mrow>
<mml:mo stretchy="true">(</mml:mo>
<mml:mn>0.5</mml:mn>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi mathvariant="italic">MDL</mml:mi>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:msqrt>
</mml:math>
</disp-formula>
<p>Where Unc represents the uncertainty of the target pollutant, MDL represents the detection limit of the target pollutant, Conc represents the concentration of the target pollutant, and Er represents the error fraction. If Conc &#x2264; MDL, Unc is calculated using <xref ref-type="disp-formula" rid="EQ2">Equation 2</xref>; if Conc&#x202F;&#x003E;&#x202F;MDL, Unc is calculated using <xref ref-type="disp-formula" rid="EQ3">Equation 3</xref>. In this study, Er was set to 10%.</p>
</sec>
<sec id="sec7">
<title>Health risk assessment</title>
<p>This study employed the &#x2018;four-step&#x2019; health risk assessment model recommended by the U. S. Environmental Protection Agency (EPA) to evaluate the non-carcinogenic and carcinogenic risks of metals and metalloids in PM<sub>2.5</sub> in Wuxi City. The &#x2018;four-step&#x2019; health risk assessment model includes hazard identification, dose&#x2013;response assessment, exposure assessment, and risk characterization. The toxicological parameters of various metals and metalloids were obtained from Agency for Toxic Substances and Disease Registry (ATSDR) (<xref ref-type="bibr" rid="ref38">38</xref>), The California Environmental Protection Agency (CALEPA) (<xref ref-type="bibr" rid="ref39">39</xref>), the Integrated Risk Information System (IRIS) (<xref ref-type="bibr" rid="ref40">40</xref>), the Provisional Peer Reviewed Toxicity Values (PPRTVs) (<xref ref-type="bibr" rid="ref41">41</xref>), the US EPA&#x2019;s National Ambient Air Quality Standards (NAAQS), and other related documents (<xref ref-type="bibr" rid="ref42">42</xref>), as summarized in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>. The health risk assessment for metals and metalloids entering the human body via inhalation was calculated using <xref ref-type="disp-formula" rid="EQ4">Equations 4</xref><xref ref-type="disp-formula" rid="EQ5"/><xref ref-type="disp-formula" rid="EQ6">&#x2013;</xref><xref ref-type="disp-formula" rid="EQ7">7</xref>.</p>
<disp-formula id="EQ4">
<label>(4)</label>
<mml:math id="M4">
<mml:mtext>Average Daily Doses</mml:mtext>
<mml:mspace width="0.25em"/>
<mml:mo stretchy="true">(</mml:mo>
<mml:mi>ADD</mml:mi>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">C</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>ED</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>EF</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>ET</mml:mi>
</mml:mrow>
<mml:mi>AT</mml:mi>
</mml:mfrac>
</mml:math>
</disp-formula>
<disp-formula id="EQ5">
<label>(5)</label>
<mml:math id="M5">
<mml:mtext>Lifetime Average Daily Doses</mml:mtext>
<mml:mspace width="0.25em"/>
<mml:mo stretchy="true">(</mml:mo>
<mml:mtext>LADD</mml:mtext>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">C</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>ED</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>EF</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>ET</mml:mi>
</mml:mrow>
<mml:mi>LT</mml:mi>
</mml:mfrac>
</mml:math>
</disp-formula>
<disp-formula id="EQ6">
<label>(6)</label>
<mml:math id="M6">
<mml:mtext>Hazard Quotient</mml:mtext>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mi>ADD</mml:mi>
<mml:mrow>
<mml:mi>RfC</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:msup>
<mml:mn>10</mml:mn>
<mml:mn>6</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</disp-formula>
<disp-formula id="EQ7">
<label>(7)</label>
<mml:math id="M7">
<mml:msub>
<mml:mtext>RISK</mml:mtext>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mtext>LADD</mml:mtext>
<mml:mo>&#x00D7;</mml:mo>
<mml:mi>IUR</mml:mi>
<mml:mo>&#x00D7;</mml:mo>
<mml:msup>
<mml:mn>10</mml:mn>
<mml:mrow>
<mml:mo>&#x2212;</mml:mo>
<mml:mn>3</mml:mn>
</mml:mrow>
</mml:msup>
</mml:math>
</disp-formula>
<p>Where ADD shows the chronic non-carcinogenic daily exposure dose via inhalation (ng/m<sup>3</sup>), and LADD denotes the daily exposure dose to carcinogenic substances via inhalation routes (ng/m<sup>3</sup>). C is the concentration of each element (ng/m<sup>3</sup>), ED is the exposure duration&#x2014;set at 30&#x202F;years according to the ATSDR recommendations. EF illustrates the exposure frequency (EF&#x202F;=&#x202F;365&#x202F;days/year), and ET is the exposure time (24&#x202F;h/day). AT is the average exposure time, which, in this study, is 30&#x202F;years for chronic non-carcinogenic influences, while LT denotes the lifetime exposure period for carcinogenic effects, set at 70&#x202F;years. HQ is the non-carcinogenic risk of a heavy metal (unitless) (<xref ref-type="bibr" rid="ref43">43</xref>), RfC is the reference concentration for chronic non-carcinogenic effects through inhalation (mg/m<sup>3</sup>), and IUR is the inhalation unit risk (&#x03BC;g/m<sup>3</sup>)<sup>&#x2212;1</sup>. Finally, RISK defines the carcinogenic risk (unitless) (<xref ref-type="bibr" rid="ref32">32</xref>, <xref ref-type="bibr" rid="ref34">34</xref>).</p>
<p>Research has shown that if HQ&#x003C;1 (<xref ref-type="bibr" rid="ref34">34</xref>), the non-carcinogenic risk is approximately unlikely or negligible. However, If HQ&#x202F;&#x2265;&#x202F;1, a potential health risk is defined. Similarly, if RISK &#x003C; 10<sup>&#x2212;6</sup> (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref44">44</xref>), indicates the carcinogenic risk is negligible. A RISK value between 10<sup>&#x2212;6</sup> and 10<sup>&#x2212;4</sup> is considered within acceptable range (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref44">44</xref>), whereas the carcinogenic risk is acceptable, and if the value of RISK&#x003E; 10<sup>&#x2212;4</sup>, it indicates that the carcinogenic risk exceeds acceptable risk limits.</p>
</sec>
<sec id="sec8">
<title>Data analysis</title>
<p>All the analyses were conducted using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Concentration values below the detection limit were replaced with half of the detection limit. Element concentrations are expressed as means &#x00B1; SD or quartiles [median (P25, P75)]. According to the concentration distribution, one-way ANOVA followed by the Scheffe&#x2019;s test or Kruskal-Wallis H test followed by Nemenyi test were used for comparison between years and seasons. Spearman&#x2019;s correlation analysis was used to examine the correlation between various metals and PM<sub>2.5</sub>, as well as meteorological parameters. <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05 was set as statistically significant in the study. The seasons were defined as follows: spring (March to May), summer (June to August) autumn (September to November), and winter (December to February).</p>
</sec>
</sec>
<sec sec-type="results" id="sec9">
<title>Results</title>
<sec id="sec10">
<title>Long-term concentration trends and meteorological characteristics in ambient PM<sub>2.5</sub> and PM<sub>2.5</sub>-bound metals</title>
<p>In this study, the annual levels of PM<sub>2.5</sub> between 2020 and 2023 were 48.3&#x202F;&#x00B1;&#x202F;33.2, 41.1&#x202F;&#x00B1;&#x202F;25.1, 36.6&#x202F;&#x00B1;&#x202F;23.3, and 35.7&#x202F;&#x00B1;&#x202F;19.3&#x202F;&#x03BC;g/m<sup>&#x2212;3</sup>, respectively. As shown in <xref ref-type="table" rid="tab1">Table 1</xref>, the annual average ambient air PM<sub>2.5</sub> concentration during the monitoring period was 40.4&#x202F;&#x00B1;&#x202F;26.1&#x202F;&#x03BC;g/m<sup>3</sup> (n&#x202F;=&#x202F;337), with PM<sub>2.5</sub> concentrations ranging from 5.0&#x2013;166.0&#x202F;&#x03BC;g/m<sup>3</sup>. According to the references, the annual PM<sub>2.5</sub> levels in Wuxi in this study exceeded the limits of the WHO guidelines (AQG2021, 5&#x202F;&#x03BC;g/m<sup>3</sup>) and also surpassed the primary standard concentration limit of the China National Ambient Air Quality Standards (CNAAQS IT-1, 35&#x202F;&#x03BC;g/m<sup>3</sup>), but remained within the secondary standard concentration limit of the CNAAQS (IT-2, 75&#x202F;&#x03BC;g/m<sup>3</sup>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>The annual average ambient air PM<sub>2.5</sub> (&#x03BC;g/m<sup>3</sup>) and PM<sub>2.5</sub>-bound metals and metalloids concentration (ng/m<sup>3</sup>) from 2020 to 2023.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Concentration</th>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">SD</th>
<th align="center" valign="top">Min-Max</th>
<th align="center" valign="top">P50 (P25-P75)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">PM<sub>2.5</sub></td>
<td align="center" valign="middle">40.4</td>
<td align="center" valign="middle">26.1</td>
<td align="center" valign="middle">5.00&#x2013;166.0</td>
<td align="center" valign="middle">34.0 (24.0&#x2013;49.0)</td>
</tr>
<tr>
<td align="left" valign="middle">Al</td>
<td align="center" valign="middle">111.9</td>
<td align="center" valign="middle">83.9</td>
<td align="center" valign="middle">14.2&#x2013;601.7</td>
<td align="center" valign="middle">87.0 (56.4&#x2013;142.1)</td>
</tr>
<tr>
<td align="left" valign="middle">Cr</td>
<td align="center" valign="middle">3.75</td>
<td align="center" valign="middle">1.78</td>
<td align="center" valign="middle">0.28&#x2013;10.9</td>
<td align="center" valign="middle">3.49 (2.44&#x2013;4.74)</td>
</tr>
<tr>
<td align="left" valign="middle">Mn</td>
<td align="center" valign="middle">31.5</td>
<td align="center" valign="middle">16.4</td>
<td align="center" valign="middle">4.93&#x2013;94.2</td>
<td align="center" valign="middle">29.0 (19.4&#x2013;38.7)</td>
</tr>
<tr>
<td align="left" valign="middle">Ni</td>
<td align="center" valign="middle">2.88</td>
<td align="center" valign="middle">1.45</td>
<td align="center" valign="middle">0.30&#x2013;9.20</td>
<td align="center" valign="middle">2.69 (1.88&#x2013;3.70)</td>
</tr>
<tr>
<td align="left" valign="middle">As</td>
<td align="center" valign="middle">3.80</td>
<td align="center" valign="middle">2.08</td>
<td align="center" valign="middle">0.62&#x2013;9.77</td>
<td align="center" valign="middle">3.27 (2.24&#x2013;4.82)</td>
</tr>
<tr>
<td align="left" valign="middle">Se</td>
<td align="center" valign="middle">2.76</td>
<td align="center" valign="middle">1.45</td>
<td align="center" valign="middle">0.59&#x2013;9.40</td>
<td align="center" valign="middle">2.47 (1.75&#x2013;3.41)</td>
</tr>
<tr>
<td align="left" valign="middle">Cd</td>
<td align="center" valign="middle">0.68</td>
<td align="center" valign="middle">0.52</td>
<td align="center" valign="middle">0.10&#x2013;3.78</td>
<td align="center" valign="middle">0.52 (0.35&#x2013;0.87)</td>
</tr>
<tr>
<td align="left" valign="middle">Sb</td>
<td align="center" valign="middle">2.11</td>
<td align="center" valign="middle">1.33</td>
<td align="center" valign="middle">0.33&#x2013;8.65</td>
<td align="center" valign="middle">1.80 (1.22&#x2013;2.44)</td>
</tr>
<tr>
<td align="left" valign="middle">Tl</td>
<td align="center" valign="middle">0.16</td>
<td align="center" valign="middle">0.13</td>
<td align="center" valign="middle">0.02&#x2013;0.93</td>
<td align="center" valign="middle">0.12 (0.07&#x2013;0.19)</td>
</tr>
<tr>
<td align="left" valign="middle">Pb</td>
<td align="center" valign="middle">24.1</td>
<td align="center" valign="middle">15.2</td>
<td align="center" valign="middle">6.03&#x2013;97.0</td>
<td align="center" valign="middle">20.3 (14.2&#x2013;29.1)</td>
</tr>
<tr>
<td align="left" valign="middle">Li</td>
<td align="center" valign="middle">0.46</td>
<td align="center" valign="middle">0.30</td>
<td align="center" valign="middle">0.37&#x2013;3.29</td>
<td align="center" valign="middle">0.37 (0.37&#x2013;0.37)</td>
</tr>
<tr>
<td align="left" valign="middle">V</td>
<td align="center" valign="middle">0.97</td>
<td align="center" valign="middle">0.60</td>
<td align="center" valign="middle">0.04&#x2013;3.71</td>
<td align="center" valign="middle">0.87 (0.44&#x2013;1.28)</td>
</tr>
<tr>
<td align="left" valign="middle">Cu</td>
<td align="center" valign="middle">13.1</td>
<td align="center" valign="middle">9.76</td>
<td align="center" valign="middle">1.01&#x2013;71.7</td>
<td align="center" valign="middle">10.3 (7.39&#x2013;14.5)</td>
</tr>
<tr>
<td align="left" valign="middle">Zn</td>
<td align="center" valign="middle">110.3</td>
<td align="center" valign="middle">67.4</td>
<td align="center" valign="middle">9.65&#x2013;536.0</td>
<td align="center" valign="middle">94.5 (70.0&#x2013;136.7)</td>
</tr>
<tr>
<td align="left" valign="middle">Ba</td>
<td align="center" valign="middle">10.0</td>
<td align="center" valign="middle">7.55</td>
<td align="center" valign="middle">1.49&#x2013;60.4</td>
<td align="center" valign="middle">8.24 (5.77&#x2013;12.3)</td>
</tr>
<tr>
<td align="left" valign="middle">Co</td>
<td align="center" valign="middle">0.32</td>
<td align="center" valign="middle">0.21</td>
<td align="center" valign="middle">0.03&#x2013;1.13</td>
<td align="center" valign="middle">0.29 (0.11&#x2013;0.43)</td>
</tr>
<tr>
<td align="left" valign="middle">Rb</td>
<td align="center" valign="middle">0.86</td>
<td align="center" valign="middle">0.51</td>
<td align="center" valign="middle">0.16&#x2013;3.96</td>
<td align="center" valign="middle">0.74 (0.51&#x2013;1.08)</td>
</tr>
<tr>
<td align="left" valign="middle">Fe</td>
<td align="center" valign="middle">330.4</td>
<td align="center" valign="middle">174.2</td>
<td align="center" valign="middle">37.6&#x2013;1027.9</td>
<td align="center" valign="middle">287.2 (202.5&#x2013;421.1)</td>
</tr>
<tr>
<td align="left" valign="middle">Sr</td>
<td align="center" valign="middle">2.77</td>
<td align="center" valign="middle">1.81</td>
<td align="center" valign="middle">0.03&#x2013;10.2</td>
<td align="center" valign="middle">2.25 (1.54&#x2013;3.50)</td>
</tr>
<tr>
<td align="left" valign="middle">Mo</td>
<td align="center" valign="middle">3.42</td>
<td align="center" valign="middle">2.18</td>
<td align="center" valign="middle">0.03&#x2013;13.2</td>
<td align="center" valign="middle">3.01 (1.87&#x2013;4.49)</td>
</tr>
<tr>
<td align="left" valign="middle">Ag</td>
<td align="center" valign="middle">0.19</td>
<td align="center" valign="middle">0.19</td>
<td align="center" valign="middle">0.01&#x2013;1.82</td>
<td align="center" valign="middle">0.15 (0.12&#x2013;0.15)</td>
</tr>
<tr>
<td align="left" valign="middle">Sn</td>
<td align="center" valign="middle">3.40</td>
<td align="center" valign="middle">2.20</td>
<td align="center" valign="middle">0.31&#x2013;15.6</td>
<td align="center" valign="middle">2.92 (1.94&#x2013;4.19)</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>When concentration was less than CNAAQS IT-2, it was defined as a clean day. The average PM<sub>2.5</sub> concentration on clean days was 34.6&#x202F;&#x00B1;&#x202F;15.6&#x202F;&#x03BC;g/m<sup>3</sup>. During the monitoring period, samples exceeding the CNAAQS IT-2 accounted for 1.85% of the annual days and 8.01% of the total samples. Additionally, 156 samples exceeded the CNAAQS IT-1, accounting for 46.3% of the total samples. Furthermore, we calculated that the number of days exceeding the AQG 2021 was 12 times greater than the number of days exceeding the CNAAQS IT-2.</p>
<p><xref ref-type="fig" rid="fig1">Figures 1A</xref>,<xref ref-type="fig" rid="fig1">B</xref> present the annual and seasonal distribution characteristics of PM<sub>2.5</sub> exceeding CNAAQS IT-2 and CNAAQS IT-1. According to the CNAAQS IT-2, the proportion of days exceeding the standard to the total monitoring days were 13.10, 11.76, 4.76 and 2.38%, respectively. For CNAAQS IT-1, the exceedance percentages were 58.33, 45.88, 42.86 and 38.10%, respectively, with the percentage of exceeding decreasing each year from 2020 to 2023.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Temporal distribution and exceedance characteristics of PM<sub>2.5</sub>. <bold>(A)</bold> The annual and seasonal distribution characteristics of PM<sub>2.5</sub> exceeding CNAAQS IT-2 from 2020 to 2023. <bold>(B)</bold> The annual and seasonal distribution characteristics of PM<sub>2.5</sub> exceeding CNAAQS IT-1 from 2020 to 2023. <bold>(C)</bold> The annual and seasonal distribution characteristics of PM<sub>2.5</sub> between 2020 and 2023.</p>
</caption>
<graphic xlink:href="fpubh-13-1599702-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Grouped bar charts display data related to air pollution. Chart A shows exceedance percentages for PM levels greater than seventy-five micrograms per cubic meter by year and season, with a significant decrease by 2023 and a peak in winter. Chart B illustrates percentages for levels over thirty-five micrograms, trending down yearly but peaking again in winter. Chart C presents average PM2.5 concentrations, decreasing through 2023 with elevated levels in winter.</alt-text>
</graphic>
</fig>
<p>The annual and seasonal distribution characteristics of PM<sub>2.5</sub> are shown in <xref ref-type="fig" rid="fig1">Figure 1C</xref>. Over the last 4&#x202F;years, the concentration of PM<sub>2.5</sub> in Wuxi has decreased with increasing years (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01), with a 26% decrease in PM<sub>2.5</sub> concentration from 2020 to 2023.</p>
<p>We found that the annual average ambient air PM<sub>2.5</sub> concentration in winter and spring was significantly higher than in summer and autumn (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05). The PM<sub>2.5</sub> concentration in summer was almost half of that observed in winter. This also confirms that wind speed, temperature, relative humidity, and precipitation were negatively correlated with PM<sub>2.5</sub> concentration (p&#x202F;&#x003C;&#x202F;0.05), while there was a positive correlation between barometric pressure and PM<sub>2.5</sub> concentration (<xref ref-type="table" rid="tab2">Table 2</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>The correlation coefficients between PM<sub>2.5</sub> and PM<sub>2.5</sub>-bound metals and metalloids concentration with meteorological parameters in Wuxi from 2020 to 2023.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Meteorological parameters</th>
<th align="center" valign="top">AP</th>
<th align="center" valign="top">T</th>
<th align="center" valign="top">RH</th>
<th align="center" valign="top">P</th>
<th align="center" valign="top">WS</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">PM<sub>2.5</sub></td>
<td align="center" valign="middle">0.33&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.46&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.062</td>
<td align="center" valign="middle">&#x2212;0.27&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.23&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Al</td>
<td align="center" valign="middle">0.30&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.31&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.29&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.37&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.054</td>
</tr>
<tr>
<td align="left" valign="middle">Cr</td>
<td align="center" valign="middle">0.036</td>
<td align="center" valign="middle">&#x2212;0.096</td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">&#x2212;0.081</td>
<td align="center" valign="middle">&#x2212;0.41&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Mn</td>
<td align="center" valign="middle">0.31&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.36&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.032</td>
<td align="center" valign="middle">&#x2212;0.17&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.33&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Ni</td>
<td align="center" valign="middle">0.15&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.17&#x002A;</td>
<td align="center" valign="middle">0.16&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.077</td>
<td align="center" valign="middle">&#x2212;0.26&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">As</td>
<td align="center" valign="middle">0.047</td>
<td align="center" valign="middle">&#x2212;0.11&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.11</td>
<td align="center" valign="middle">&#x2212;0.21&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.29&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Se</td>
<td align="center" valign="middle">&#x2212;0.037</td>
<td align="center" valign="middle">&#x2212;0.0091</td>
<td align="center" valign="middle">&#x2212;0.091</td>
<td align="center" valign="middle">&#x2212;0.28&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.42&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Cd</td>
<td align="center" valign="middle">0.16&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.22&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.22&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.27&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.15&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Sb</td>
<td align="center" valign="middle">0.12&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.21&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.21&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.37&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.39&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Tl</td>
<td align="center" valign="middle">0.22&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.31&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.26&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.33&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.16&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Pb</td>
<td align="center" valign="middle">0.42&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.56&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.11&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.22&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.22&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Li</td>
<td align="center" valign="middle">0.15&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.18&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.098</td>
<td align="center" valign="middle">&#x2212;0.12&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.23&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">V</td>
<td align="center" valign="middle">0.022</td>
<td align="center" valign="middle">&#x2212;0.013</td>
<td align="center" valign="middle">&#x2212;0.0079</td>
<td align="center" valign="middle">&#x2212;0.21&#x002A;</td>
<td align="center" valign="middle">0.011</td>
</tr>
<tr>
<td align="left" valign="middle">Cu</td>
<td align="center" valign="middle">0.044</td>
<td align="center" valign="middle">&#x2212;0.11</td>
<td align="center" valign="middle">0.020</td>
<td align="center" valign="middle">&#x2212;0.15&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.38&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Zn</td>
<td align="center" valign="middle">0.33&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.34&#x002A;</td>
<td align="center" valign="middle">0.030</td>
<td align="center" valign="middle">&#x2212;0.075</td>
<td align="center" valign="middle">&#x2212;0.30&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Ba</td>
<td align="center" valign="middle">0.44&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.49&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.11&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.24&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.13&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Co</td>
<td align="center" valign="middle">0.17&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.11</td>
<td align="center" valign="middle">0.094</td>
<td align="center" valign="middle">&#x2212;0.042</td>
<td align="center" valign="middle">&#x2212;0.13&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Rb</td>
<td align="center" valign="middle">0.38&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.48&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.25&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.30&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.19&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Fe</td>
<td align="center" valign="middle">0.28&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.30&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.021</td>
<td align="center" valign="middle">&#x2212;0.16&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.30&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Sr</td>
<td align="center" valign="middle">0.34&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.31&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.17&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.30&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.079</td>
</tr>
<tr>
<td align="left" valign="middle">Mo</td>
<td align="center" valign="middle">0.040</td>
<td align="center" valign="middle">&#x2212;0.042</td>
<td align="center" valign="middle">0.15&#x002A;</td>
<td align="center" valign="middle">0.043</td>
<td align="center" valign="middle">&#x2212;0.43&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Ag</td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">&#x2212;0.21&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.14&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.16&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.18&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Sn</td>
<td align="center" valign="middle">&#x2212;0.061</td>
<td align="center" valign="middle">&#x2212;0.066</td>
<td align="center" valign="middle">&#x2212;0.018</td>
<td align="center" valign="middle">&#x2212;0.15&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.26&#x002A;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>AP was atmospheric pressure, T was temperature, RH was relative humidity, P was precipitation and WS was wind speed; &#x002A; Showed significant correlation at 0.05 level.</p>
</table-wrap-foot>
</table-wrap>
<p>During the monitoring period, the total content of 22 elements (except Hg, Th, Be and U, which were below the detection limits) was 659.7&#x202F;&#x00B1;&#x202F;318.5&#x202F;ng/m<sup>3</sup>, comprising 1.91% mass. The total metal concentrations (TMs) decreased by 30.2%, from 783.4&#x202F;ng/m<sup>3</sup> in 2020 to 547.0&#x202F;ng/m<sup>3</sup> in 2023. The concentration range of the monitored metals and metalloids was from 0.01 to 1027.9&#x202F;ng/m<sup>3</sup>. The annual mean concentrations of PM<sub>2.5</sub>-bound metals and metalloids in Wuxi from 2020 to 2023 are shown in <xref ref-type="table" rid="tab1">Table 1</xref>. The mean concentration ranked as Fe&#x202F;&#x003E;&#x202F;Al&#x202F;&#x003E;&#x202F;Zn&#x202F;&#x003E;&#x202F;Mn&#x202F;&#x003E;&#x202F;Pb&#x202F;&#x003E;&#x202F;Cu&#x202F;&#x003E;&#x202F;Ba&#x202F;&#x003E;&#x202F;As&#x202F;&#x003E;&#x202F;Cr&#x202F;&#x003E;&#x202F;Mo&#x202F;&#x003E;&#x202F;Sn&#x202F;&#x003E;&#x202F;Ni&#x202F;&#x003E;&#x202F;Sr.&#x202F;&#x003E;&#x202F;Se&#x202F;&#x003E;&#x202F;Sb&#x202F;&#x003E;&#x202F;V&#x202F;&#x003E;&#x202F;Rb&#x202F;&#x003E;&#x202F;Cd&#x202F;&#x003E;&#x202F;Li&#x202F;&#x003E;&#x202F;Co&#x202F;&#x003E;&#x202F;Ag&#x202F;&#x003E;&#x202F;Tl. The results indicate that the main seven metals and metalloids in PM<sub>2.5</sub> in Wuxi were Fe, Al, Zn, Mn, Pb, Cu, and Ba. The annual mean concentrations of these seven dominant metals were 330.4&#x202F;&#x00B1;&#x202F;174.2&#x202F;ng/m<sup>3</sup>, 111.9&#x202F;&#x00B1;&#x202F;83.9&#x202F;ng/m<sup>3</sup>, 110.3&#x202F;&#x00B1;&#x202F;67.4&#x202F;ng/m<sup>3</sup>, 31.5&#x202F;&#x00B1;&#x202F;16.4&#x202F;ng/m<sup>3</sup>, 24.1&#x202F;&#x00B1;&#x202F;15.2&#x202F;ng/m<sup>3</sup>, 13.0&#x202F;&#x00B1;&#x202F;9.76&#x202F;ng/m<sup>3</sup>, and 10.0&#x202F;&#x00B1;&#x202F;7.55&#x202F;ng/m<sup>3</sup>, respectively, which accounted for 95.7% of TMs. The annual mean concentrations of As, Cr, Mo, Sn, Ni, Sr., Se, and Sb were below 10&#x202F;ng/m<sup>3</sup> but over 1&#x202F;ng/m<sup>3</sup>, while the concentrations of remaining PM<sub>2.5-</sub>bound metals and metalloids were less than 1&#x202F;ng/m<sup>3</sup>.</p>
<p>The annual mean concentrations of As, Cd, Hg and Pb in Wuxi were 3.80&#x202F;&#x00B1;&#x202F;2.08&#x202F;ng/m<sup>3</sup>, 0.68&#x202F;&#x00B1;&#x202F;0.52&#x202F;ng/m<sup>3</sup>, 0.1&#x202F;ng/m<sup>3</sup> and 24.1&#x202F;&#x00B1;&#x202F;15.2&#x202F;ng/m<sup>3</sup>, respectively, which are significantly less than the limits set by the CNAAQS (6&#x202F;ng/m<sup>3</sup>, 5&#x202F;ng/m<sup>3</sup>, 50&#x202F;ng/m<sup>3</sup> and 500&#x202F;ng/m<sup>3</sup>, respectively).</p>
<p>During the monitoring period over the past 4&#x202F;years, the concentrations of most metals in PM<sub>2.5</sub> showed a remarkable decline in Wuxi (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05), as shown in <xref ref-type="table" rid="tab3">Table 3</xref>. Co showed the largest reduction in concentration, dropping by 55.1%, followed by Sr. (47.6%), Cu (39.6%), Fe (33.6%), Rb (33.2%), Tl (32.9%), Zn (31.1%), Cd (30.3%), Al (29.8%), Ba (25.5%), V (21.2%), Se (19.9%), and Mn (17.1%), and others had no statistical trends (<italic>p</italic>&#x202F;&#x003E;&#x202F;0.05).</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>The annual variations of PM<sub>2.5</sub>-bound metals and metalloids (ng/m<sup>3</sup>) in Wuxi from 2020 to 2023.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Metals and metalloids</th>
<th align="center" valign="top">2020</th>
<th align="center" valign="top">2021</th>
<th align="center" valign="top">2022</th>
<th align="center" valign="top">2023</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Al</td>
<td align="center" valign="middle">135.4&#x202F;&#x00B1;&#x202F;61.8</td>
<td align="center" valign="middle">109.6&#x202F;&#x00B1;&#x202F;109.1</td>
<td align="center" valign="middle">107.4&#x202F;&#x00B1;&#x202F;67.6</td>
<td align="center" valign="middle">95.0&#x202F;&#x00B1;&#x202F;84.8</td>
</tr>
<tr>
<td align="left" valign="middle">Cr</td>
<td align="center" valign="middle">3.28&#x202F;&#x00B1;&#x202F;1.58</td>
<td align="center" valign="middle">4.07&#x202F;&#x00B1;&#x202F;1.08</td>
<td align="center" valign="middle">4.26&#x202F;&#x00B1;&#x202F;2.38</td>
<td align="center" valign="middle">3.41&#x202F;&#x00B1;&#x202F;1.70</td>
</tr>
<tr>
<td align="left" valign="middle">Mn</td>
<td align="center" valign="middle">34.3&#x202F;&#x00B1;&#x202F;17.2</td>
<td align="center" valign="middle">29.4&#x202F;&#x00B1;&#x202F;16.4</td>
<td align="center" valign="middle">33.8&#x202F;&#x00B1;&#x202F;16.2</td>
<td align="center" valign="middle">28.4&#x202F;&#x00B1;&#x202F;15.2</td>
</tr>
<tr>
<td align="left" valign="middle">Ni</td>
<td align="center" valign="middle">2.83&#x202F;&#x00B1;&#x202F;1.16</td>
<td align="center" valign="middle">2.70&#x202F;&#x00B1;&#x202F;1.18</td>
<td align="center" valign="middle">3.50&#x202F;&#x00B1;&#x202F;1.87</td>
<td align="center" valign="middle">2.48&#x202F;&#x00B1;&#x202F;1.29</td>
</tr>
<tr>
<td align="left" valign="middle">As</td>
<td align="center" valign="middle">4.18&#x202F;&#x00B1;&#x202F;2.19</td>
<td align="center" valign="middle">3.86&#x202F;&#x00B1;&#x202F;2.31</td>
<td align="center" valign="middle">3.59&#x202F;&#x00B1;&#x202F;1.91</td>
<td align="center" valign="middle">3.57&#x202F;&#x00B1;&#x202F;1.86</td>
</tr>
<tr>
<td align="left" valign="middle">Se</td>
<td align="center" valign="middle">3.14&#x202F;&#x00B1;&#x202F;1.76</td>
<td align="center" valign="middle">2.45&#x202F;&#x00B1;&#x202F;1.21</td>
<td align="center" valign="middle">2.94&#x202F;&#x00B1;&#x202F;1.48</td>
<td align="center" valign="middle">2.51&#x202F;&#x00B1;&#x202F;1.20</td>
</tr>
<tr>
<td align="left" valign="middle">Cd</td>
<td align="center" valign="middle">0.79&#x202F;&#x00B1;&#x202F;0.52</td>
<td align="center" valign="middle">0.74&#x202F;&#x00B1;&#x202F;0.53</td>
<td align="center" valign="middle">0.63&#x202F;&#x00B1;&#x202F;0.60</td>
<td align="center" valign="middle">0.55&#x202F;&#x00B1;&#x202F;0.37</td>
</tr>
<tr>
<td align="left" valign="middle">Sb</td>
<td align="center" valign="middle">2.27&#x202F;&#x00B1;&#x202F;1.34</td>
<td align="center" valign="middle">2.07&#x202F;&#x00B1;&#x202F;1.52</td>
<td align="center" valign="middle">2.07&#x202F;&#x00B1;&#x202F;1.26</td>
<td align="center" valign="middle">2.02&#x202F;&#x00B1;&#x202F;1.17</td>
</tr>
<tr>
<td align="left" valign="middle">Tl</td>
<td align="center" valign="middle">0.20&#x202F;&#x00B1;&#x202F;0.13</td>
<td align="center" valign="middle">0.16&#x202F;&#x00B1;&#x202F;0.16</td>
<td align="center" valign="middle">0.12&#x202F;&#x00B1;&#x202F;0.07</td>
<td align="center" valign="middle">0.13&#x202F;&#x00B1;&#x202F;0.12</td>
</tr>
<tr>
<td align="left" valign="middle">Pb</td>
<td align="center" valign="middle">25.7&#x202F;&#x00B1;&#x202F;16.3</td>
<td align="center" valign="middle">24.4&#x202F;&#x00B1;&#x202F;15.6</td>
<td align="center" valign="middle">21.6&#x202F;&#x00B1;&#x202F;9.81</td>
<td align="center" valign="middle">24.72&#x202F;&#x00B1;&#x202F;17.7</td>
</tr>
<tr>
<td align="left" valign="middle">Li</td>
<td align="center" valign="middle">0.41&#x202F;&#x00B1;&#x202F;0.18</td>
<td align="center" valign="middle">0.47&#x202F;&#x00B1;&#x202F;0.23</td>
<td align="center" valign="middle">0.47&#x202F;&#x00B1;&#x202F;0.34</td>
<td align="center" valign="middle">0.50&#x202F;&#x00B1;&#x202F;0.41</td>
</tr>
<tr>
<td align="left" valign="middle">V</td>
<td align="center" valign="middle">1.07&#x202F;&#x00B1;&#x202F;0.58</td>
<td align="center" valign="middle">1.04&#x202F;&#x00B1;&#x202F;0.58</td>
<td align="center" valign="middle">0.92&#x202F;&#x00B1;&#x202F;0.64</td>
<td align="center" valign="middle">0.84&#x202F;&#x00B1;&#x202F;0.59</td>
</tr>
<tr>
<td align="left" valign="middle">Cu</td>
<td align="center" valign="middle">16.12&#x202F;&#x00B1;&#x202F;9.90</td>
<td align="center" valign="middle">14.31&#x202F;&#x00B1;&#x202F;10.14</td>
<td align="center" valign="middle">12.0&#x202F;&#x00B1;&#x202F;10.6</td>
<td align="center" valign="middle">9.74&#x202F;&#x00B1;&#x202F;6.86</td>
</tr>
<tr>
<td align="left" valign="middle">Zn</td>
<td align="center" valign="middle">129.7&#x202F;&#x00B1;&#x202F;85.9</td>
<td align="center" valign="middle">120.7&#x202F;&#x00B1;&#x202F;62.3</td>
<td align="center" valign="middle">101.3&#x202F;&#x00B1;&#x202F;55.3</td>
<td align="center" valign="middle">89.3&#x202F;&#x00B1;&#x202F;54.8</td>
</tr>
<tr>
<td align="left" valign="middle">Ba</td>
<td align="center" valign="middle">12.0&#x202F;&#x00B1;&#x202F;5.77</td>
<td align="center" valign="middle">10.2&#x202F;&#x00B1;&#x202F;4.60</td>
<td align="center" valign="middle">9.08&#x202F;&#x00B1;&#x202F;8.12</td>
<td align="center" valign="middle">8.91&#x202F;&#x00B1;&#x202F;10.2</td>
</tr>
<tr>
<td align="left" valign="middle">Co</td>
<td align="center" valign="middle">0.43&#x202F;&#x00B1;&#x202F;0.21</td>
<td align="center" valign="middle">0.45&#x202F;&#x00B1;&#x202F;0.15</td>
<td align="center" valign="middle">0.19&#x202F;&#x00B1;&#x202F;0.14</td>
<td align="center" valign="middle">0.19&#x202F;&#x00B1;&#x202F;0.17</td>
</tr>
<tr>
<td align="left" valign="middle">Rb</td>
<td align="center" valign="middle">1.05&#x202F;&#x00B1;&#x202F;0.65</td>
<td align="center" valign="middle">0.82&#x202F;&#x00B1;&#x202F;0.5</td>
<td align="center" valign="middle">0.88&#x202F;&#x00B1;&#x202F;0.45</td>
<td align="center" valign="middle">0.70&#x202F;&#x00B1;&#x202F;0.35</td>
</tr>
<tr>
<td align="left" valign="middle">Fe</td>
<td align="center" valign="middle">400.5&#x202F;&#x00B1;&#x202F;198.0</td>
<td align="center" valign="middle">369.8&#x202F;&#x00B1;&#x202F;167.6</td>
<td align="center" valign="middle">284.9&#x202F;&#x00B1;&#x202F;140.9</td>
<td align="center" valign="middle">265.9&#x202F;&#x00B1;&#x202F;150.1</td>
</tr>
<tr>
<td align="left" valign="middle">Sr</td>
<td align="center" valign="middle">3.69&#x202F;&#x00B1;&#x202F;1.79</td>
<td align="center" valign="middle">3.23&#x202F;&#x00B1;&#x202F;1.82</td>
<td align="center" valign="middle">2.23&#x202F;&#x00B1;&#x202F;1.48</td>
<td align="center" valign="middle">1.94&#x202F;&#x00B1;&#x202F;1.58</td>
</tr>
<tr>
<td align="left" valign="middle">Mo</td>
<td align="center" valign="middle">3.17&#x202F;&#x00B1;&#x202F;2.23</td>
<td align="center" valign="middle">3.47&#x202F;&#x00B1;&#x202F;1.89</td>
<td align="center" valign="middle">4.01&#x202F;&#x00B1;&#x202F;2.24</td>
<td align="center" valign="middle">3.03&#x202F;&#x00B1;&#x202F;2.27</td>
</tr>
<tr>
<td align="left" valign="middle">Ag</td>
<td align="center" valign="middle">0.13&#x202F;&#x00B1;&#x202F;0.08</td>
<td align="center" valign="middle">0.20&#x202F;&#x00B1;&#x202F;0.21</td>
<td align="center" valign="middle">0.17&#x202F;&#x00B1;&#x202F;0.09</td>
<td align="center" valign="middle">0.24&#x202F;&#x00B1;&#x202F;0.28</td>
</tr>
<tr>
<td align="left" valign="middle">Sn</td>
<td align="center" valign="middle">3.14&#x202F;&#x00B1;&#x202F;1.52</td>
<td align="center" valign="middle">2.88&#x202F;&#x00B1;&#x202F;1.28</td>
<td align="center" valign="middle">4.79&#x202F;&#x00B1;&#x202F;3.08</td>
<td align="center" valign="middle">2.80&#x202F;&#x00B1;&#x202F;1.87</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The seasonal distribution characteristics of metals and metalloids in PM<sub>2.5</sub> are shown in <xref ref-type="fig" rid="fig2">Figure 2</xref>. The annual average concentrations of Pb, Zn, Ba, Ag, and Rb were significantly higher in winter compared to spring, summer, and autumn (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05). Additionally, the annual average concentrations of Al, Cd, Sb, Tl and Sr. were notably higher in winter than in summer and autumn (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01). The annual average concentrations of Mn, Ni, and As were significantly higher in winter than in summer (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05). Furthermore, the annual average concentrations of Al, Mn, Sb, Tl, Pb, Ba, Rb, Fe, Sr, and Sn were significantly higher in spring than in summer (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05). The annual average concentrations of Cd, Tl, Pb, Rb, Mo, and Sn were significantly higher in spring compared to autumn (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01). Lastly, the annual average concentrations of Mn, Ni, Zn, Fe, and Mo were significantly higher in autumn than in summer (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05), while no significant statistical trends were observed for other elements (<italic>p</italic>&#x202F;&#x003E;&#x202F;0.05). Compared to spring, the mean concentration of Al, Mn, Sb, Tl, Pb, Ba, Rb, Fe, Sr. and Sn was 68.4, 41.0, 36.0, 61.3, 65.4, 71.6, 76.7, 45.8, 58.8, and 29.4% higher in summer, respectively. In contrast, the average concentration of Pb, Zn, Ba, Ag, Rb, Al, Cd, Sb, Tl, Sr., Mn, Ni, and As was 143.5, 80.1, 140.0, 69.2, 114.3, 85.6, 44.7, 61.7, 86.7, 88.2, 57.8, 29.4, and 27.9% higher in winter than in summer, respectively.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Seasonal distribution characteristics 16 PM<sub>2.5-</sub>bound metals and metalloids in the atmosphere with statistical significance.</p>
</caption>
<graphic xlink:href="fpubh-13-1599702-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Sixteen bar graphs showing seasonal concentrations of different elements in ng/m&#x00B3;. Each graph represents an element with data for spring (Spr), summer (Sum), autumn (Aut), and winter (Win). Elements include aluminum, manganese, nickel, arsenic, cadmium, antimony, thallium, lead, zinc, barium, rubidium, iron, strontium, molybdenum, silver, and tin. Concentration levels vary by season for each element.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec11">
<title>Correlation and source analysis of PM<sub>2.5</sub> and PM<sub>2.5</sub>-bound metals</title>
<p>The enrichment factor (EF) method is widely used to determine the sources of elements in atmospheric particulate matter, distinguishing between natural and anthropogenic factors. By utilizing the EF method, researchers can evaluate the extent to which human activities&#x2014;such as industrial emissions and transportation&#x2014;influence metals in the atmosphere. This assessment is crucial for analyzing air quality and developing environmental policies. The specific distribution of EFs for PM<sub>2.5</sub>-bound metals and metalloids in Wuxi, as showed in this study, is detailed in <xref ref-type="fig" rid="fig3">Figure 3</xref>.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>The distribution of EFs for 22 metals and metalloids in the atmosphere during the monitoring period.</p>
</caption>
<graphic xlink:href="fpubh-13-1599702-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Scatter plot showing enrichment factor (EF) of various elements on a logarithmic scale. Elements on the x-axis include Cr, Mn, Ni, As, Se, Cd, Sb, Tl, Pb, Li, V, Cu, Zn, Ba, Co, Rb, Fe, Sr, Mo, Ag, and Sn. EFs range from 1 to over 100,000, with notable high values for Cd, Sb, and Pb.</alt-text>
</graphic>
</fig>
<p>Among the 22 monitored metals and metalloids in PM<sub>2.5</sub>, all except Fe, Rb, and the reference element Al displayed EF values higher than 10, indicating significant anthropogenic pollution during the monitoring period. Notably, the EF values for Se, Mo, Cd, Ag, Sb, Zn, Sn, Pb, Cu, As, and Tl exceeded 100, with mean values of 32823.3&#x202F;&#x00B1;&#x202F;20769.2, 9578.7&#x202F;&#x00B1;&#x202F;8559.9, 5938.6&#x202F;&#x00B1;&#x202F;4623.4, 3343.5&#x202F;&#x00B1;&#x202F;4051.8, 2404.6&#x202F;&#x00B1;&#x202F;1628.9, 1833.7&#x202F;&#x00B1;&#x202F;1318.2, 1352.4&#x202F;&#x00B1;&#x202F;1145.7, 1330.1&#x202F;&#x00B1;&#x202F;936.7, 538.7&#x202F;&#x00B1;&#x202F;411.6, 333.2&#x202F;&#x00B1;&#x202F;260.7, and 254.4&#x202F;&#x00B1;&#x202F;179.3, respectively. These significantly high EFs indicate essential anthropogenic pollution and high levels of enrichment. Additionally, the EF values for Ni, Mn, Cr, Sr., Co, Ba, Li, and V ranged between 10 and 100, showing that these metals were mainly influenced by anthropogenic sources and exhibited moderate enrichment. Meanwhile, Rb and Fe had EF values between 1 and 10, indicating contributions from both natural and anthropogenic sources. Since all metals and metalloids analyzed in this study had EF values higher than 1, they need careful consideration. Further studies into their sources and potential risks are needed to better evaluate their effects.</p>
<p>The correlation analysis of PM<sub>2.5</sub> and PM<sub>2.5</sub>-bound metals and metalloids in Wuxi during the monitoring period is presented in <xref ref-type="fig" rid="fig4">Figure 4</xref>. As shown, the annual mean concentrations of the 22&#x202F;PM<sub>2.5</sub>-bound metals and metalloids exhibited positive correlations with PM<sub>2.5</sub> concentrations, with the correlation coefficients all higher than 0 (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05). Meanwhile, the correlation coefficients of Mn with Ni, Zn, Fe; Sb with As, Se, Pb; Tl with Cd, Pb, Rb with Sb; Pb with Rb; Zn with Fe; Sr. with Ba were above 0.7 (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05). Additionally, the correlation coefficients of Zn with Cu; Cr with Mn, Ni; Cd with Pb were above 0.6 (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05), indicating strong correlations. In statistics, the stronger the correlation, the greater the likelihood of a common pollution source.</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Heat map of correlation coefficient matrix between PM<sub>2.5</sub> and detected metals and metalloids.</p>
</caption>
<graphic xlink:href="fpubh-13-1599702-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Correlation matrix displaying coefficients between PM2.5 and various elements such as Al, Cr, Mn, and Ni. The scale ranges from -1 (blue) indicating strong negative correlation to 1 (red) for strong positive correlation. Values are displayed in a color gradient within the matrix, with a key provided on the right.</alt-text>
</graphic>
</fig>
<p>To investigate the specific sources of metals in PM<sub>2.5</sub>, PMF5.0 was applied to analyze the sources of PM<sub>2.5</sub>-bound metals and metalloids in Wuxi. Four distinct sources were identified by comparing spectral variations across different factors and performing orthogonal matrix rotation operations. As shown in <xref ref-type="fig" rid="fig5">Figure 5</xref>, the contribution rates of these four factors were 32.4, 26.2, 27.9, and 13.5%, respectively. The contribution rates of elements such as Cr (76.5%), Mo (62.7%), Ni (62.2%), Li (60%), and Mn (51.3%) in factor 1 were significantly high. Among these, Cr, Mo, Ni, and Mn primarily originate from industries such as automobile manufacturing and electronics production (<xref ref-type="bibr" rid="ref45">45</xref>). Given the strong presence of the electronic information industry, precision machinery and mechatronics, and automotive parts manufacturing in Wuxi, Factor 1 was identified as an industrial source. The contribution rates of As (51.9%), Se (42.6%), Cd (56.4%), Sb (51.9%), Tl (62.2%), and Pb (43.6%) in Factor 2 were also remarkable. Moreover, strong correlations were observed between As and Se, as well as Cd and Pb&#x2014;metals commonly associated with fuel combustion (<xref ref-type="bibr" rid="ref46">46</xref>, <xref ref-type="bibr" rid="ref47">47</xref>). Therefore, Factor 2 was identified as a combustion source. In Factor 3, Zn (63.8%), Co (53.4%), and Cu (52.2%), showed high contributions. Zn is linked to motor vehicle exhaust and rubber tire wear, Cu generates from gasoline and diesel vehicle emissions, and Co is primarily used in battery manufacturing for electric vehicles and e-bikes. Hence, factor 3 was identified as a source of automotive emissions. Factor 4 was characterized by a higher rate of Al (69.0%) remarkably exceeding that of other elements. Since Al is the most abundant metal in the Earth&#x2019;s crust (<xref ref-type="bibr" rid="ref36">36</xref>) Factor 4 was identified as a natural dust source.</p>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>PMF analysis results showing source contributions of PM2.5-bound metals and metalloids in Wuxi.</p>
</caption>
<graphic xlink:href="fpubh-13-1599702-g005.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Stacked bar chart showing species concentration percentages for various elements. Bars are divided into four factors: blue (Factor 1), red (Factor 2), green (Factor 3), and yellow (Factor 4). Elements include Al, Cr, Mn, Ni, among others, with varying concentrations for each factor.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec12">
<title>Health risks</title>
<p>Based on the &#x201C;four-step&#x201D; health risk assessment model and the reference toxicity parameters for metals and metalloids, the non-carcinogenic risk for adults exposed to 14 elements in PM<sub>2.5</sub>, and the carcinogenic risk (RISK) for adults exposed to 5 elements in PM<sub>2.5</sub> via inhalation were calculated. Regarding non-carcinogenic risks (<xref ref-type="fig" rid="fig6">Figure 6</xref>), the HQs of Sb, Al, As, Be, Cd, Cr (VI), Hg, Pb, Mn, Ni, Se, Co, Cu, Mo, V and Zn were all less than 1, illustrating no essential non-carcinogenic risk. However, Mn showed the highest non-carcinogenic risk among all other metals and metalloids, with an HQ of 6.29&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>&#x202F;&#x00B1;&#x202F;3.28&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>, followed by As (2.53&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>&#x202F;&#x00B1;&#x202F;1.39&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>) and (Pb 1.61&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>&#x202F;&#x00B1;&#x202F;1.01&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;1</sup>).</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>Non-carcinogenic and carcinogenic risks associated with inhalation exposure to PM<sub>2.5</sub>-bound metals and metalloids.</p>
</caption>
<graphic xlink:href="fpubh-13-1599702-g006.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Two bar charts compare elements by hazard quotient (HQ) and risk. Left chart shows Mn with the highest HQ, followed by As. Right chart shows As with the highest risk, followed by Cr(6+).</alt-text>
</graphic>
</fig>
<p>For carcinogenic risks (<xref ref-type="fig" rid="fig6">Figure 6</xref>), the RISK values of Cd, Pb, and Ni were all bellow threshold of 1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup> during the monitoring period, with values of 5.21&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>&#x202F;&#x00B1;&#x202F;4.02&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>, 1.24&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>&#x202F;&#x00B1;&#x202F;7.79&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;8</sup>, and 3.21&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>&#x202F;&#x00B1;&#x202F;1.62&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;7</sup>, respectively. However, it is important to note that the RISK values of Cr (VI) and As both exceeded 1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>, with values 2.76&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>&#x202F;&#x00B1;&#x202F;1.31&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>, 7.00&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>&#x202F;&#x00B1;&#x202F;3.83&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>, respectively, indicating potential carcinogenic concern.</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec13">
<title>Discussion</title>
<p>Air pollution has become a major public concern due to its association with various diseases, including lung cancer, cardiovascular disease, bladder cancer, childhood leukemia, dementia, and immune system disorders, all of which can contribute to abnormal death (<xref ref-type="bibr" rid="ref48 ref49 ref50 ref51">48&#x2013;51</xref>). According to WHO, approximately 2.4 million people die each year due to the health effects of air pollution (<xref ref-type="bibr" rid="ref52">52</xref>). Among various pollutants, PM<sub>2.5</sub> is considered one of the most important environmental risk factors, contributing to cardiovascular disease (<xref ref-type="bibr" rid="ref53">53</xref>), reduced childhood intelligence (<xref ref-type="bibr" rid="ref50">50</xref>), asthma (<xref ref-type="bibr" rid="ref54">54</xref>), chronic obstructive pulmonary disease (<xref ref-type="bibr" rid="ref55">55</xref>), and allergic disease (<xref ref-type="bibr" rid="ref56">56</xref>).</p>
<p>During the monitoring period, the concentration of PM<sub>2.5</sub> in Wuxi showed a significant downward trend, indicating that air pollution control measures have had a certain effect. Additionally, the proportion of clean days relative to the total number of monitoring days increased compared to the standard, suggesting that the emission reduction efforts in Wuxi have been effective over the past 4&#x202F;years. This enhancement can be attributed to the implementation of &#x201C;Action Plan for Air Pollution Prevention and Control&#x201D; and the &#x201C;Three Year Action Plan for Winning the Blue Sky Defense War.&#x201D; In recent years, numerous policies have been continuously introduced across China to improve air quality and protect public health, with considerable results. For instance, the annual average concentration of PM<sub>2.5</sub> in China decreased from 61.8&#x202F;&#x03BC;g/m<sup>3</sup> to 42.0&#x202F;&#x03BC;g/m<sup>3</sup> in 2017 (<xref ref-type="bibr" rid="ref57">57</xref>). Similarly, in Suzhou, located southeast China, the average PM<sub>2.5</sub> concentration declined from 51.2&#x202F;&#x00B1;&#x202F;30.1&#x202F;&#x03BC;g/m<sup>3</sup> (2019) to 43.9&#x202F;&#x00B1;&#x202F;25.0&#x202F;&#x03BC;g/m<sup>3</sup> (2021) (<xref ref-type="bibr" rid="ref34">34</xref>). In the Beijing-Tianjin-Hebei (BTH) Region in northern China, the annual average PM<sub>2.5</sub> concentration decreased from 98.9&#x202F;&#x03BC;g/m<sup>3</sup> in 2013 to 64.9&#x202F;&#x03BC;g/m<sup>3</sup> in 2017 (<xref ref-type="bibr" rid="ref58">58</xref>).</p>
<p>However, the annual average concentration of PM<sub>2.5</sub> in Wuxi was 40.4&#x202F;&#x00B1;&#x202F;26.1&#x202F;&#x03BC;g/m<sup>3</sup>, which still exceeded both the CNAAQS IT-1 (35&#x202F;&#x03BC;g/m<sup>3</sup>) and the WHO guideline of 5&#x202F;&#x03BC;g/m<sup>3</sup>. This indicates that the air pollution remains concerning and requires serious attention.</p>
<p>It was showed that PM<sub>2.5</sub> concentrations in winter and spring were significantly higher than those in summer and autumn (<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05). This seasonal trend is consistent with findings from Suzhou (<xref ref-type="bibr" rid="ref34">34</xref>) and Hangzhou (<xref ref-type="bibr" rid="ref59">59</xref>), and may be attributed to lower temperature, reduced precipitation, lower wind speeds and lower relative humidity during winter and spring, resulting in difficult for pollutants to disperse. Therefore, environmental protection departments should focus on atmospheric conditions during these patterns and increase monitoring frequency accordingly.</p>
<p>During the monitoring period, Fe, Al and Zn were identified as the main components of PM2.5-bound metals and metalloids in Wuxi. It is well known that Al and Fe are the third and fourth most common elements in the nature, accounting for approximately 7.73 and 4.75% of its total weight, respectively. In contrast, Zn is mainly associated with tire wear, vehicle exhaust emissions, and the use of Zn in rubber products. This is particularly related in Wuxi, where, as of 2023, there were almost 2.72 million registered vehicles and approximately 105 rubber factories, contributing increasingly to Zn emissions.</p>
<p>The annual mean concentrations of As, Cd, Hg and Pb in Wuxi were significantly lower than the limits set by the CNAAQS. However, it is noteworthy that the annual mean concentrations of total Cr were 3.75&#x202F;&#x00B1;&#x202F;1.78&#x202F;ng/m<sup>3</sup>. Although previous studies showed that Cr (VI) accounts for about 1/7 of total Cr (<xref ref-type="bibr" rid="ref60">60</xref>), the estimated concentration of Cr (VI) still exceeded the CNAAQS (0.025 ng/m<sup>3</sup>) by a factor of 20. Cr (VI) is classified as a Class I carcinogen by the International Agency for Research on Cancer (IARC) and enter the human body through ingestion, inhalation or touch with skin and mucous membranes. Therefore, it is necessary to pay special attention to Cr (VI) exposure. In this study Cr (VI) concentration were defined based on literature. Future research should focus on speciation analysis of chromium to provide a more accurate risk assessment associated with Cr (VI).</p>
<p>The concentrations of Co, Sr., Cu, Fe, Rb, Tl, Zn, Cd, Al, Ba, V, Se, and Mn in PM<sub>2.5</sub> showed a decreasing trend to varying degrees, as analyzed by Scheffe&#x2019;s test or the Kruskal Wallis H test, as Co showed the largest reduction in concentration, followed by Sr. and Cu This trend was consistent with the decline in PM<sub>2.5</sub>, as positive correlations were observed between PM<sub>2.5</sub>-bound metals and metalloids and PM<sub>2.5</sub> in Wuxi. This may be due to the implementation of the Action Plan for Air Pollution Prevention and Control and the Three Year Action Plan for Winning the Blue Sky Defense War. Among them, measures such as reducing coal combustion, promoting green travel, and using clean energy have led to a decrease in PM<sub>2.5</sub> related metal concentrations. The seasonal patterns of metals and metalloids in PM<sub>2.5</sub> can be attributable to source emissions, meteorological factors, anthropogenic activities, and environmental transport. As shown, the concentrations of PM<sub>2.5</sub>-bound metals and metalloids in Wuxi exhibited obvious seasonality. In addition to meteorological factors, this may be due to the increased use of coal-fired heating in spring and winter, which leads to significant emissions of sulfates and nitrates. These emissions improve the acidity of droplets, increasing the solubility of metal elements and making them more readily captured by particulate matter (<xref ref-type="bibr" rid="ref61 ref62 ref63">61&#x2013;63</xref>).</p>
<p>In this study, the EFs of Se, Mo, Cd, Ag, Sb, Zn, Sn, Pb, Cu, As, and Tl were above 100, indicating that these elements were mainly influenced by anthropogenic sources, exhibiting significant high enrichment and severe pollution, which needs attention. On the other hand, Ni, Mn, Cr, Sr., Co, Ba, Li, and V showed moderate enrichment. Spearman correlation analysis revealed strong correlations between various pairs of metals, such as Mn and Ni, Zn, Fe; Sb and As, Se, Pb; Tl and Cd, Pb, Rb and Sb; Pb and Rb; Zn and Fe; Sr. and Ba; Zn and Cu; Cr and Mn, Ni; Cd and Pb. Based on the EFs and these correlation results, the particular sources of Se, Mo, Cd, Sb, Zn, Pb, Cu, As, Tl, Ni, Mn, Cr, Sr., Co, Ba, and Li needed special attention.</p>
<p>To accurately identify the sources of metal pollutants, the PMF5.0 was utilized in this study. As illustrated in <xref ref-type="fig" rid="fig5">Figure 5</xref>, the contribution rates of industrial emissions, automotive emissions, fuel emissions, and dust during the monitoring period in Wuxi were 32.4, 27.9, 26.2, and 13.5%, respectively. According to the Statistical Bulletin on National Economic and Social Development of Wuxi City, from 2020 to 2023, the added value of industrial enterprises above designated size reached 396.9, 492.6, 558.6 and 600.5 billion RMB, increasing by 6.60, 12.9, 5.40 and 7.80% year-on-year, respectively. Industry plays an essential role in Wuxi&#x2019;s economic and social development. However, as a major source of PM<sub>2.5</sub>-bound metal pollution, it is necessary to optimize the industrial structure and accelerate industrial transformation while maintaining development. The contributions from automobile emissions and combustion suggest the importance of promoting green transportation, clean energy, and transitioning from thermal power to wind and hydropower. Notably, a waste incineration power plant has been built in the east area of Wuxi, which helps control city&#x2019;s living garbage and provides a cleaner, recyclable energy source compared to conventional electric power generation. Additionally, to control soil dust pollution, attention should be made to enforce civilized construction practices, promote enclosure engineering, harden exposed roads, and wet operations measures.</p>
<p>Regarding non-carcinogenic risks, the HQs of Sb, Al, As, Be, Cd, Cr (VI), Hg, Pb, Mn, Ni, Se, Co, Cu, Mo, V and Zn were all below 1 in Wuxi, indicating that long-term exposure to these metals and metalloids is unlikely to cause adverse health effects. Moreover, the non-carcinogenic risks for residents in Wuxi were lower than other cities in southeastern China, such as Hangzhou and Ningbo (<xref ref-type="bibr" rid="ref59">59</xref>).</p>
<p>However, it is noteworthy that the risk from inhalation accounts for only a small portion compared to oral and skin exposure routes (<xref ref-type="bibr" rid="ref64">64</xref>). Therefore, although the non-carcinogenic risk from inhalation remains within an acceptable range, the overall risk of human exposure to various environmental elements should not be overlooked.</p>
<p>As Mn has the highest HQ among all elements in this study, its excessive accumulation in the central nervous system may lead to neurotoxicity, cussing brain disease (<xref ref-type="bibr" rid="ref65 ref66 ref67">65&#x2013;67</xref>). And the disturbance of manganese homeostasis caused by excessive intake of manganese is related to the occurrence of osteoporosis, obesity, type 2 diabetes/insulin resistance, non-alcoholic fatty liver, atherosclerosis and other diseases (<xref ref-type="bibr" rid="ref68">68</xref>). Co-exposure to manganese, lead, and chromium may exacerbate oxidative stress (<xref ref-type="bibr" rid="ref69">69</xref>). Al, which had the second highest annual average concentration during monitoring period, has been associated with Alzheimer&#x2019;s disease, epilepsy and autism (<xref ref-type="bibr" rid="ref70">70</xref>). As a result, the potential health risks of Mn and Al should not be ignored, and more studies are needed to explore their non-carcinogenic influences on local residents. Notably, the HQs value of Mn in spring and winter compared to summer and autumn, indicating that the non-carcinogenic risks to Wuxi residents posed by Mn in colder seasons need particular attention.</p>
<p>For carcinogenic risks, the RISK values of Cd, Pb, and Ni were below 1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup> for all seasons during the monitoring duration, while the RISK values of Cr (VI) and As were between 1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup> and 1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;4</sup>. This illustrates that the RISK of Cd, Pb, and Ni via inhalation was negligible.</p>
<p>As the RISK value of Cr (VI) and As were higher than 1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup> for all the seasons. Excessive intake of As will lead to skin cancer, lung cancer, bladder cancer, liver cancer, cardiovascular disease and nervous system damage (<xref ref-type="bibr" rid="ref71">71</xref>, <xref ref-type="bibr" rid="ref72">72</xref>). It was indicated that combined effect of As and Cd were synergistic effect, and Cd may potentiate As nephrotoxicity during the long-term (<xref ref-type="bibr" rid="ref73">73</xref>). Cr (VI) is related with carcinogenic, genotoxic, and mutagenic effects (<xref ref-type="bibr" rid="ref74">74</xref>, <xref ref-type="bibr" rid="ref75">75</xref>). Chronic exposure to Ni, Cr (VI) or As has long been known to increase cancer incidence among affected individuals (<xref ref-type="bibr" rid="ref76">76</xref>). Therefore potential risks to human body after long-term exposure should be taken seriously.</p>
<p>Moreover, the combined effects of exposure multi-metal cannot be ignored. In summary, Cr (VI) and As could be considered key carcinogenic risk factors affecting the health of local residents, which was consistent with previous observations in Suzhou (<xref ref-type="bibr" rid="ref34">34</xref>) and Zhejiang Province (<xref ref-type="bibr" rid="ref59">59</xref>). Hence, it is necessary for the local government to take pertinent actions to control the emission sources of PM<sub>2.5</sub>-bound metals and metalloids.</p>
</sec>
<sec sec-type="conclusions" id="sec14">
<title>Conclusion</title>
<p>This study provides a four-year reference for experimental and field studies on PM<sub>2.5</sub>. The annual average concentration of PM<sub>2.5</sub> in Wuxi from 2020 to 2023 was 40.42&#x202F;&#x00B1;&#x202F;26.11&#x202F;&#x03BC;g/m<sup>3</sup>, showing a downward trend over the years. This shows that Wuxi has achieved certain results in environmental protection and energy conservation over the past 4&#x202F;years. However, although the annual mean concentration of PM<sub>2.5</sub> in Wuxi met the CNAAQS IT-2 standard, there was still a gap compared to CNAAQS IT-1 and WHO standards, suggesting that the energy conservation and emission reduction should not be overlooked. During the monitoring period, the concentrations of PM<sub>2.5</sub> indicated clear seasonal distribution characteristics, with levels in winter and spring significantly higher than those in summer and autumn.</p>
<p>Fe, Al, Zn, Mn, Pb, Cu, and Ba were seven dominant metals in PM<sub>2.5</sub> accounted for 95.7% of TMs. During the monitoring period over the past 4&#x202F;years, the concentrations of most metals in PM<sub>2.5</sub> showed a remarkable decline, as Co showed the largest reduction in concentration, dropping by 55.1%, followed by Sr. and Cu. Seasonal variation could observed in Pb, Zn, Ba, Ag, Rb, Al, Cd, Sb, Tl, Sr., Mn, Ni, and As, with higher concentration in winter.</p>
<p>PMF analysis revealed that metals in PM<sub>2.5</sub> in Wuxi City mainly generated from fossil fuels combustion, industrial pollution, vehicle emissions, and construction dust pollution. The sequence of non-carcinogenic metals by their mean HQ values was found to be Mn&#x202F;&#x003E;&#x202F;As &#x003E; Pb&#x202F;&#x003E;&#x202F;Cd&#x202F;&#x003E;&#x202F;Co&#x202F;&#x003E;&#x202F;Ni&#x202F;&#x003E;&#x202F;Al&#x202F;&#x003E;&#x202F;V&#x202F;&#x003E;&#x202F;Mo&#x202F;&#x003E;&#x202F;Sb&#x202F;&#x003E;&#x202F;Cr (VI)&#x202F;&#x003E;&#x202F;Zn&#x202F;&#x003E;&#x202F;Cu&#x202F;&#x003E;&#x202F;Se. And the sequence of carcinogenic metals by their mean RISK values was found to be As &#x003E; Cr (VI)&#x202F;&#x003E;&#x202F;Cd&#x202F;&#x003E;&#x202F;Ni&#x202F;&#x003E;&#x202F;Pb, During the monitoring period.</p>
<p>Considering both non-carcinogenic risk and carcinogenic factors, the risk levels of individual elements monitored during the study were within acceptable range according to EPA. However, it is worth noting that PM<sub>2.5</sub> in the air can enter the human body not only through inhalation but also through both oral and skin exposure. Hence, long-term exposure risks multiple pathways should not be ignored, especially for Mn, which had the highest HQ in the monitored metals.</p>
<p>Moreover, the RISK values of As and Cr (VI) were above 1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;6</sup>, indicating that the potential health risks from long-term exposure cannot be ignored. It is also noteworthy that even after conversion by 1/7, the concentration of Cr (VI) still exceeded the CNAAQS standard. This highlights the need for the accurate detection of Cr (VI) concentrations to precisely estimate its risk, as it remained the most hazardous metal during the monitoring period.</p>
<p>This study suggests that future efforts should not only continue promoting clean energy, green transportation, civilized construction and decreasing industrial pollution emissions to reduce the PM<sub>2.5</sub> concentration, but also increase the frequency of air monitoring in winter and spring. Additionally, targeted measures should be taken to reduce the concentrations of Cr (VI), As and Mn, to better protect the health of local residents in Wuxi.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec15">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec sec-type="author-contributions" id="sec16">
<title>Author contributions</title>
<p>LC: Writing &#x2013; original draft, Formal analysis. XuZ: Investigation, Writing &#x2013; original draft, Methodology. XiZ: Data curation, Writing &#x2013; review &#x0026; editing, Funding acquisition. YG: Validation, Writing &#x2013; review &#x0026; editing, Supervision. LK: Funding acquisition, Writing &#x2013; review &#x0026; editing, Supervision. YW: Writing &#x2013; review &#x0026; editing, Software, Visualization. WL: Writing &#x2013; review &#x0026; editing, Supervision, Conceptualization. PZ: Writing &#x2013; review &#x0026; editing, Project administration.</p>
</sec>
<sec sec-type="funding-information" id="sec17">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Nos. 22075106), Medical Key Discipline Program of Wuxi Health Commission (Nos. LCZX2021006 and CXTD2021004), and Open Fund Project of Hubei Provincial Key Laboratory for Occupational Hazard Identification and Control in 2023 (Nos. OHIC2023Z03).</p>
</sec>
<sec sec-type="COI-statement" id="sec18">
<title>Conflict of interest</title>
<p>The 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="sec19">
<title>Generative AI statement</title>
<p>The author(s) declare that no Gen AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
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<title>Publisher&#x2019;s note</title>
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<sec sec-type="supplementary-material" id="sec21">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fpubh.2025.1599702/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fpubh.2025.1599702/full#supplementary-material</ext-link></p>
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