Comparison of Short-Term Associations between PM2.5 Components and Mortality across Six Major Cities in South Korea

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} div.type-section h2 { font-size: 20px; line-height: 26px; font-weight: 300; } div.type-section h3 { margin-left: 15px; margin-bottom: 0px; font-weight: 300; } .journal-tabs .tab-title.active a { } </style> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/slick.css?f38b2db10e01b157?1732615622"> <meta name="title" content="Comparison of Short-Term Associations between PM2.5 Components and Mortality across Six Major Cities in South Korea"> <meta name="description" content="Association between short-term exposure to fine particulate matter (PM2.5) and mortality or morbidity varies geographically, and this variation could be due to different chemical composition affected by local sources. However, there have been only a few Asian studies possibly due to limited monitoring data. Using nationwide regulatory monitoring data of PM2.5 chemical components in South Korea, we aimed to compare the associations between daily exposure to PM2.5 components and mortality across six major cities. We obtained daily 24-h concentrations of PM2.5 and 11 PM2.5 components measured from 2013 to 2015 at single sites located in residential areas. We used death certificate data to compute the daily counts of nonaccidental, cardiovascular, and respiratory deaths. Using the generalized additive model, we estimated relative risks of daily mortality for an interquartile range increase in each pollutant concentration, while controlling for a longer-term time trend and meteorology. While elemental carbon was consistently associated with nonaccidental mortality across all cities, nickel and vanadium were strongly associated with respiratory or cardiovascular mortality in Busan and Ulsan, two large port cities. Our study shows that PM2.5 components responsible for PM2.5-associated mortality differed across cities depending on the dominant pollution sources, such as traffic and oil combustion." > <link rel="image_src" href="https://pub.mdpi-res.com/img/journals/ijerph-logo.png?f4a7c3c190986910" > <meta name="dc.title" content="Comparison of Short-Term Associations between PM2.5 Components and Mortality across Six Major Cities in South Korea"> <meta name="dc.creator" content="Si-Eun Yoo"> <meta name="dc.creator" content="Jin-Soo Park"> <meta name="dc.creator" content="Soo Hyun Lee"> <meta name="dc.creator" content="Choong-Hee Park"> <meta name="dc.creator" content="Chul-Woo Lee"> <meta name="dc.creator" content="Sang-Bo Lee"> <meta name="dc.creator" content="Seung Do Yu"> <meta name="dc.creator" content="Sun-Young Kim"> <meta name="dc.creator" content="Ho Kim"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal of Environmental Research and Public Health 2019, Vol. 16, Page 2872"> <meta name="dc.date" content="2019-08-11"> <meta name ="dc.identifier" content="10.3390/ijerph16162872"> <meta name="dc.publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf" > <meta name="dc.language" content="en" > <meta name="dc.description" content="Association between short-term exposure to fine particulate matter (PM2.5) and mortality or morbidity varies geographically, and this variation could be due to different chemical composition affected by local sources. However, there have been only a few Asian studies possibly due to limited monitoring data. Using nationwide regulatory monitoring data of PM2.5 chemical components in South Korea, we aimed to compare the associations between daily exposure to PM2.5 components and mortality across six major cities. We obtained daily 24-h concentrations of PM2.5 and 11 PM2.5 components measured from 2013 to 2015 at single sites located in residential areas. We used death certificate data to compute the daily counts of nonaccidental, cardiovascular, and respiratory deaths. Using the generalized additive model, we estimated relative risks of daily mortality for an interquartile range increase in each pollutant concentration, while controlling for a longer-term time trend and meteorology. While elemental carbon was consistently associated with nonaccidental mortality across all cities, nickel and vanadium were strongly associated with respiratory or cardiovascular mortality in Busan and Ulsan, two large port cities. Our study shows that PM2.5 components responsible for PM2.5-associated mortality differed across cities depending on the dominant pollution sources, such as traffic and oil combustion." > <meta name="dc.subject" content="chemical component" > <meta name="dc.subject" content="fine particle" > <meta name="dc.subject" content="mortality" > <meta name="dc.subject" content="short-term" > <meta name="dc.subject" content="time-series study" > <meta name ="prism.issn" content="1660-4601"> <meta name ="prism.publicationName" content="International Journal of Environmental Research and Public Health"> <meta name ="prism.publicationDate" content="2019-08-11"> <meta name ="prism.volume" content="16"> <meta name ="prism.number" content="16"> <meta name ="prism.section" content="Article" > <meta name ="prism.startingPage" content="2872" > <meta name="citation_issn" content="1660-4601"> <meta name="citation_journal_title" content="International Journal of Environmental Research and Public Health"> <meta name="citation_publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="citation_title" content="Comparison of Short-Term Associations between PM2.5 Components and Mortality across Six Major Cities in South Korea"> <meta name="citation_publication_date" content="2019/1"> <meta name="citation_online_date" content="2019/08/11"> <meta name="citation_volume" content="16"> <meta name="citation_issue" content="16"> <meta name="citation_firstpage" content="2872"> <meta name="citation_author" content="Yoo, Si-Eun"> <meta name="citation_author" content="Park, Jin-Soo"> <meta name="citation_author" content="Lee, Soo Hyun"> <meta name="citation_author" content="Park, Choong-Hee"> <meta name="citation_author" content="Lee, Chul-Woo"> <meta name="citation_author" content="Lee, Sang-Bo"> <meta name="citation_author" content="Yu, Seung Do"> <meta name="citation_author" content="Kim, Sun-Young"> <meta name="citation_author" content="Kim, Ho"> <meta name="citation_doi" content="10.3390/ijerph16162872"> <meta name="citation_id" content="mdpi-ijerph16162872"> <meta name="citation_abstract_html_url" content="https://www.mdpi.com/1660-4601/16/16/2872"> <meta name="citation_pdf_url" content="https://www.mdpi.com/1660-4601/16/16/2872/pdf?version=1565660007"> <link rel="alternate" type="application/pdf" title="PDF Full-Text" href="https://www.mdpi.com/1660-4601/16/16/2872/pdf?version=1565660007"> <meta name="fulltext_pdf" content="https://www.mdpi.com/1660-4601/16/16/2872/pdf?version=1565660007"> <meta name="citation_fulltext_html_url" content="https://www.mdpi.com/1660-4601/16/16/2872/htm"> <link rel="alternate" type="text/html" title="HTML Full-Text" href="https://www.mdpi.com/1660-4601/16/16/2872/htm"> <meta name="fulltext_html" content="https://www.mdpi.com/1660-4601/16/16/2872/htm"> <link rel="alternate" type="text/xml" title="XML Full-Text" href="https://www.mdpi.com/1660-4601/16/16/2872/xml"> <meta name="fulltext_xml" content="https://www.mdpi.com/1660-4601/16/16/2872/xml"> <meta name="citation_xml_url" content="https://www.mdpi.com/1660-4601/16/16/2872/xml"> <meta name="twitter:card" content="summary" /> <meta name="twitter:site" content="@MDPIOpenAccess" /> <meta name="twitter:image" content="https://pub.mdpi-res.com/img/journals/ijerph-logo-social.png?f4a7c3c190986910" /> <meta property="fb:app_id" content="131189377574"/> <meta property="og:site_name" content="MDPI"/> <meta property="og:type" content="article"/> <meta property="og:url" content="https://www.mdpi.com/1660-4601/16/16/2872" /> <meta property="og:title" content="Comparison of Short-Term Associations between PM2.5 Components and Mortality across Six Major Cities in South Korea" /> <meta property="og:description" content="Association between short-term exposure to fine particulate matter (PM2.5) and mortality or morbidity varies geographically, and this variation could be due to different chemical composition affected by local sources. However, there have been only a few Asian studies possibly due to limited monitoring data. Using nationwide regulatory monitoring data of PM2.5 chemical components in South Korea, we aimed to compare the associations between daily exposure to PM2.5 components and mortality across six major cities. We obtained daily 24-h concentrations of PM2.5 and 11 PM2.5 components measured from 2013 to 2015 at single sites located in residential areas. We used death certificate data to compute the daily counts of nonaccidental, cardiovascular, and respiratory deaths. Using the generalized additive model, we estimated relative risks of daily mortality for an interquartile range increase in each pollutant concentration, while controlling for a longer-term time trend and meteorology. While elemental carbon was consistently associated with nonaccidental mortality across all cities, nickel and vanadium were strongly associated with respiratory or cardiovascular mortality in Busan and Ulsan, two large port cities. Our study shows that PM2.5 components responsible for PM2.5-associated mortality differed across cities depending on the dominant pollution sources, such as traffic and oil combustion." /> <meta property="og:image" content="https://pub.mdpi-res.com/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g001-550.jpg?1566880528" /> <link rel="alternate" type="application/rss+xml" title="MDPI Publishing - Latest articles" href="https://www.mdpi.com/rss"> <meta name="google-site-verification" content="PxTlsg7z2S00aHroktQd57fxygEjMiNHydKn3txhvwY"> <meta name="facebook-domain-verification" content="mcoq8dtq6sb2hf7z29j8w515jjoof7" /> <script id="Cookiebot" data-cfasync="false" src="https://consent.cookiebot.com/uc.js" data-cbid="51491ddd-fe7a-4425-ab39-69c78c55829f" type="text/javascript" async></script>  <script type="text/plain" data-cookieconsent="statistics"> (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); 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data-dropdown='profile-card-drop2020586' data-options='is_hover:true, hover_timeout:5000'> Si-Eun Yoo</div><div id="profile-card-drop2020586" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Si-Eun Yoo</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/author/aTBSYXRva2VscEhiNmRaK1hoRUphdz09?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Si-Eun%20Yoo" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Si-Eun%20Yoo&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Si-Eun%20Yoo" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 1,2</sup>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop2020587' data-options='is_hover:true, hover_timeout:5000'> Jin-Soo Park</div><div id="profile-card-drop2020587" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Jin-Soo Park</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/author/Rm8zTE1ERkw0Rmd6RkRaN3Q0YWZlNld5b2lhNDRLdkpZb2ZJem9DV2R5dz0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Jin-Soo%20Park" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Jin-Soo%20Park&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Jin-Soo%20Park" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 3</sup>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop2020588' data-options='is_hover:true, hover_timeout:5000'> Soo Hyun Lee</div><div id="profile-card-drop2020588" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Soo Hyun Lee</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/author/UmFzaFlZUEtlYm5YZW1LMjJKcUlMRVJYUVgvaE16YWlKSkpTN0Y0VVhUOD0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Soo%20Hyun%20Lee" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Soo%20Hyun%20Lee&field1=authors" 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class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Chul-Woo Lee</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/author/QjFJYkRRRnIxVXJ0MHJQTWVXRy81OVpyV2VkR1ZtaU93ZFNONmV0aDZqMD0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Chul-Woo%20Lee" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Chul-Woo%20Lee&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Chul-Woo%20Lee" 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height:6px;"></span> <div></div> <div style="margin: 5px 0 15px 0;" class="hypothesis_container"> <div class="art-affiliations"> <div class="affiliation "> <div class="affiliation-item"><sup>1</sup></div> <div class="affiliation-name ">Environmental Health Research Division, National Institute of Environmental Research, Incheon 22689, Korea</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>2</sup></div> <div class="affiliation-name ">Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>3</sup></div> <div class="affiliation-name ">Air Quality Research Division, National Institute of Environmental Research, Incheon 22689, Korea</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>4</sup></div> <div class="affiliation-name ">Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Gyeonngi 10408, Korea</div> </div> <div class="affiliation"> <div class="affiliation-item"><sup>*</sup></div> <div class="affiliation-name ">Author to whom correspondence should be addressed. </div> </div> </div> </div> <div class="bib-identity" style="margin-bottom: 10px;"> <em>Int. J. Environ. Res. Public Health</em> <b>2019</b>, <em>16</em>(16), 2872; <a href="https://doi.org/10.3390/ijerph16162872">https://doi.org/10.3390/ijerph16162872</a> </div> <div class="pubhistory" style="font-weight: bold; padding-bottom: 10px;"> <span style="display: inline-block">Submission received: 28 May 2019</span> / <span style="display: inline-block">Revised: 6 August 2019</span> / <span style="display: inline-block">Accepted: 6 August 2019</span> / <span style="display: inline-block">Published: 11 August 2019</span> </div> <div class="highlight-box1"> <div class="download"> <a class="button button--color-inversed button--drop-down" data-dropdown="drop-download-256756" aria-controls="drop-supplementary-256756" aria-expanded="false"> Download <i class="material-icons">keyboard_arrow_down</i> </a> <div id="drop-download-256756" class="f-dropdown label__btn__dropdown label__btn__dropdown--button" data-dropdown-content aria-hidden="true" tabindex="-1"> <a class="UD_ArticlePDF" href="/1660-4601/16/16/2872/pdf?version=1565660007" data-name="Comparison of Short-Term Associations between PM2.5 Components and Mortality across Six Major Cities in South Korea" data-journal="ijerph">Download PDF</a> <br/> <a id="js-pdf-with-cover-access-captcha" href="#" data-target="/1660-4601/16/16/2872/pdf-with-cover" class="accessCaptcha">Download PDF with Cover</a> <br/> <a id="js-xml-access-captcha" href="#" data-target="/1660-4601/16/16/2872/xml" class="accessCaptcha">Download XML</a> <br/> <a href="/1660-4601/16/16/2872/epub" id="epub_link">Download Epub</a> <br/> <a href="javascript:void(0);" data-reveal-id="supplementaryModal">Download Supplementary Material</a> <br/> </div> <div class="js-browse-figures" style="display: inline-block;"> <a href="#" class="button button--color-inversed margin-bottom-10 openpopupgallery UI_BrowseArticleFigures" data-target='article-popup' data-counterslink = "https://www.mdpi.com/1660-4601/16/16/2872/browse" >Browse Figures</a> </div> <div id="article-popup" class="popupgallery" style="display: inline; line-height: 200%"> <a href="https://pub.mdpi-res.com/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g001.png?1566880528" title=" <strong>Figure 1</strong><br/> <p>Map of the six major cities (Seoul, Daejeon, Gwangju, Daegu, Ulsan, and Busan) and six PM<sub>2.5</sub> speciation network monitoring sites in South Korea (nine provinces in black: Gyeonggi, Gangwon, Chungbuk, Chungnam, Gyeongbuk, Gyeongnam, Jeonbuk, Jeonnam, and Jeju).</p> "> </a> <a href="https://pub.mdpi-res.com/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png?1566880528" title=" <strong>Figure 2</strong><br/> <p>Relative risks (RRs) and 95% confidence intervals of daily nonaccidental, cardiovascular and respiratory mortality for interquartile range increases (IQR) in daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components across the six South Korean major cities from 2013 to 2015.</p> "> </a> </div> <a class="button button--color-inversed" href="/1660-4601/16/16/2872/notes">Versions Notes</a> </div> </div> <div class="responsive-moving-container small hidden" data-id="article-counters" style="margin-top: 15px;"></div> <div class="html-dynamic"> <section> <div class="art-abstract art-abstract-new in-tab hypothesis_container"> <p> <div><section class="html-abstract" id="html-abstract"> <h2 id="html-abstract-title">Abstract</h2><b>:</b> <div class="html-p">Association between short-term exposure to fine particulate matter (PM<sub>2.5</sub>) and mortality or morbidity varies geographically, and this variation could be due to different chemical composition affected by local sources. However, there have been only a few Asian studies possibly due to limited monitoring data. Using nationwide regulatory monitoring data of PM<sub>2.5</sub> chemical components in South Korea, we aimed to compare the associations between daily exposure to PM<sub>2.5</sub> components and mortality across six major cities. We obtained daily 24-h concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> components measured from 2013 to 2015 at single sites located in residential areas. We used death certificate data to compute the daily counts of nonaccidental, cardiovascular, and respiratory deaths. Using the generalized additive model, we estimated relative risks of daily mortality for an interquartile range increase in each pollutant concentration, while controlling for a longer-term time trend and meteorology. While elemental carbon was consistently associated with nonaccidental mortality across all cities, nickel and vanadium were strongly associated with respiratory or cardiovascular mortality in Busan and Ulsan, two large port cities. Our study shows that PM<sub>2.5</sub> components responsible for PM<sub>2.5</sub>-associated mortality differed across cities depending on the dominant pollution sources, such as traffic and oil combustion.</div> </section> <div id="html-keywords"> <div class="html-gwd-group"><div id="html-keywords-title">Keywords: </div><a href="/search?q=chemical+component">chemical component</a>; <a href="/search?q=fine+particle">fine particle</a>; <a href="/search?q=mortality">mortality</a>; <a href="/search?q=short-term">short-term</a>; <a href="/search?q=time-series+study">time-series study</a></div> <div> </div> </div> </div> </p> </div> </section> </div> <div class="hypothesis_container"> <ul class="menu html-nav" data-prev-node="#html-quick-links-title"> </ul> <div class="html-body"> <section id='sec1-ijerph-16-02872' type='intro'><h2 data-nested='1'> 1. Introduction</h2><div class='html-p'>Epidemiological studies suggest that the association between short-term exposure to fine particulate matter (PM<sub>2.5</sub>) and mortality or morbidity is possibly due to the association with specific chemical components [<a href="#B1-ijerph-16-02872" class="html-bibr">1</a>,<a href="#B2-ijerph-16-02872" class="html-bibr">2</a>,<a href="#B3-ijerph-16-02872" class="html-bibr">3</a>,<a href="#B4-ijerph-16-02872" class="html-bibr">4</a>,<a href="#B5-ijerph-16-02872" class="html-bibr">5</a>]. Ambient PM<sub>2.5</sub> is a mixture of many chemical components including carbon, trace elements, and inorganic ions. These components originate from different pollution sources, and therefore, identifying specific components responsible for the health effect of PM<sub>2.5</sub> is of particular interest and expected to provide guidance for policy solutions. For example, elemental carbon (EC) largely emitted from diesel exhaust was found to be strongly associated with mortality and cardiovascular endpoints compared to other components in some epidemiological studies [<a href="#B1-ijerph-16-02872" class="html-bibr">1</a>,<a href="#B2-ijerph-16-02872" class="html-bibr">2</a>,<a href="#B6-ijerph-16-02872" class="html-bibr">6</a>,<a href="#B7-ijerph-16-02872" class="html-bibr">7</a>,<a href="#B8-ijerph-16-02872" class="html-bibr">8</a>]. These findings support policy efforts targeting reduction in diesel emissions by developing efficient fuel and installation of emission control devices. </div><div class='html-p'>As emission sources geographically differ, the relative chemical components’ composition of PM<sub>2.5</sub> varies across areas, and their health effects could also vary accordingly. A review of 41 worldwide studies on PM<sub>2.5</sub> chemical components and mortality showed substantial regional differences in effect estimates, suggesting higher risks of PM<sub>2.5</sub> and EC in North America and Europe, subsequently, than in the West Pacific [<a href="#B1-ijerph-16-02872" class="html-bibr">1</a>]. In addition, some of the chemical components observed with relatively high concentrations in some areas also showed strong associations with mortality or morbidity in the same areas. In the National Morbidity, Mortality, and Air Pollution Study including 69 U.S. communities, the overall mortality effect estimate of PM<sub>10</sub> decreased substantially when they excluded communities in New York City where nickel (Ni) and vanadium (V) concentrations were particularly high [<a href="#B9-ijerph-16-02872" class="html-bibr">9</a>,<a href="#B10-ijerph-16-02872" class="html-bibr">10</a>]. A study performed in two U.S. cities showed higher concentrations of potassium (K), Ni, and EC in Seattle than in Detroit, and found stronger associations of these components with nonaccidental mortality in Seattle [<a href="#B5-ijerph-16-02872" class="html-bibr">5</a>]. However, there is insufficient evidence to conclude whether the components with higher concentrations and/or higher contribution to PM<sub>2.5</sub> have greater toxicity compared to other components. </div><div class='html-p'>Different PM<sub>2.5</sub> chemical compositions and health effects have gained increasing attention in Asian countries, where PM<sub>2.5</sub> concentrations are high—possibly driven by diverse pollution sources related to rapid urban development—and could affect large populations [<a href="#B11-ijerph-16-02872" class="html-bibr">11</a>,<a href="#B12-ijerph-16-02872" class="html-bibr">12</a>]. However, only a few single-city studies have been conducted in Asia, primarily due to the lack of PM<sub>2.5</sub> chemical component data [<a href="#B11-ijerph-16-02872" class="html-bibr">11</a>,<a href="#B13-ijerph-16-02872" class="html-bibr">13</a>,<a href="#B14-ijerph-16-02872" class="html-bibr">14</a>,<a href="#B15-ijerph-16-02872" class="html-bibr">15</a>,<a href="#B16-ijerph-16-02872" class="html-bibr">16</a>,<a href="#B17-ijerph-16-02872" class="html-bibr">17</a>]. The South Korean government established nationwide PM<sub>2.5</sub> chemical speciation networks in 2011 in order to identify major sources of PM<sub>2.5</sub> and monitor the level of chemical component concentrations [<a href="#B18-ijerph-16-02872" class="html-bibr">18</a>]. The networks include at least one monitoring site in each major city and have sampled daily concentrations of various PM<sub>2.5</sub> chemical components. This large volume of PM<sub>2.5</sub> chemical component data provides a unique opportunity to compare their compositions and health effects in multiple cities with diverse pollution sources. </div><div class='html-p'>In this study, we aimed to compare the associations of short-term exposure to PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components to daily mortality across six cities. Specifically, we estimated the association in each of the six major South Korean cities from 2013 through to 2015, and compared the associations across six cities by 12 pollutants and three types of cause-specific mortality. </div></section><section id='sec2-ijerph-16-02872' type='methods'><h2 data-nested='1'> 2. Methods</h2><section id='sec2dot1-ijerph-16-02872' type=''><h4 class='html-italic' data-nested='2'> 2.1. Study Area</h4><div class='html-p'>We used six PM<sub>2.5</sub> chemical speciation sites located as a single site in each of the six South Korean major cities: Seoul, Daejeon, Gwangju, Daegu, Ulsan, and Busan (<a href="#ijerph-16-02872-f001" class="html-fig">Figure 1</a> and <a href="#app1-ijerph-16-02872" class="html-app">Table S1</a>). South Korea is comprised of seven metropolitan cities and nine provinces. We chose six cities as our study areas where extended time-series data of PM<sub>2.5</sub> components are available. The average area and population of the six major cities in 2015 were 726.8 km<sup>2</sup> (range = 501.2, 1060.8) and 3,364,715 people (range = 1,173,534, 10,022,181). Seoul is the capital of South Korea, located in the central part of the Korean peninsula. Busan has grown as the largest seaport in South Korea, and is an economic center of the southeast. Daegu is situated in a basin surrounded by mountains, and has developed into a city with machinery, metal, and textile industries. Gwangju is located in the southwest region, close to expansive agricultural fields. Daejeon is a commercial and industrial city in the central region. Ulsan is another port city located in the southeastern part and well known as its heavy and chemical industries.</div></section><section id='sec2dot2-ijerph-16-02872' type=''><h4 class='html-italic' data-nested='2'> 2.2. PM<sub>2.5</sub> and PM<sub>2.5</sub> Components</h4><div class='html-p'>In South Korea, nation-wide air quality regulatory monitoring networks for criteria pollutants including PM<sub>10</sub>, NO<sub>2</sub>, SO<sub>2</sub>, CO, and ozone were established in 1995. The monitoring networks have expanded to include other pollutants such as PM<sub>2.5</sub> and PM<sub>2.5</sub> chemical components, and currently consist of approximately 300 sites throughout the country [<a href="#B19-ijerph-16-02872" class="html-bibr">19</a>]. The PM<sub>2.5</sub> chemical speciation networks were established in 2011 and have been operated by the Ministry of Environment. Whereas the reference measurement method for PM<sub>2.5</sub> is the beta-ray absorption method in South Korea, PM<sub>2.5</sub> chemical speciation networks quantify PM<sub>2.5</sub> concentrations using the gravimetric method and specifically focus on the collection of a wide range of PM<sub>2.5</sub> chemical components. In 2015, 22 PM<sub>2.5</sub> chemical speciation network sites collected approximately 30 PM<sub>2.5</sub> components including carbon, inorganic ions, and metallic elements. Twelve sites are located in seven major cities, while 10 sites are in nine provinces. </div><div class='html-p'>We used daily 24-h concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> components at six chemical speciation network sites in six major cities from 2013 to 2015 obtained from the National Institute Environmental Research. Out of the 12 PM<sub>2.5</sub> speciation network sites in seven major cities, we excluded three sites that operated for less than three years and one site where heavy metal measurements were missing for two years. Two sites were additionally excluded to maximize samples, as we selected the monitors that ran for a longer period between two sites in each of Seoul and Ulsan. Our study period was restricted to three years from 2013 to 2015 where PM<sub>2.5</sub> component data are available in all six cities. We used the monitors that operated for the same time period to focus on city-to-city comparisons by avoiding any differences resulting from the samples collected for different time periods rather than at different sites. </div><div class='html-p'>The sampling and lab analyses for PM<sub>2.5</sub> and PM<sub>2.5</sub> components were performed following standardized protocols [<a href="#B20-ijerph-16-02872" class="html-bibr">20</a>]. All six speciation monitoring sites included in this study are operated and maintained directly by the Ministry of Environment. Samples were collected on a daily basis in 2011, and mostly changed to the every third day schedule from September, 2014. Two sites in Daegu and Ulsan adopted the every sixth day schedule. PM<sub>2.5</sub> samples were collected on 46.2 ± 0.25 mm Teflon filters with a sample flow rate of 16.7 L/min. PM<sub>2.5</sub> concentrations were quantified as gravitational mass measurements using a microbalance housed in a temperature and humidity controlled chamber. Inorganic ionic compounds were quantified using ion chromatography following the standard methodology for airborne particulate matter. Organic carbon (OC) and EC were collected on pured quarts fibers preheated at 650 °C for 2 hours before sampling, and quantified by the thermal/optical OC/EC analysis. Trace element concentrations were determined by energy dispersive X-ray fluorescence spectrometry. </div><div class='html-p'>Our study focused on EC, OC, nitrate (NO<sub>3</sub><sup>−</sup>), sulfate (SO<sub>4</sub><sup>2−</sup>), lead (Pb), Ni, silicon (Si), V, copper (Cu), zinc (Zn), and potassium (K). These 11 PM<sub>2.5</sub> components have been associated with various health endpoints in previous epidemiological and toxicological studies [<a href="#B1-ijerph-16-02872" class="html-bibr">1</a>,<a href="#B3-ijerph-16-02872" class="html-bibr">3</a>,<a href="#B4-ijerph-16-02872" class="html-bibr">4</a>]. In addition, these components were indicated as surrogates of specific sources, although contributing to more than one sources: EC and OC for mobile emissions of diesel and gasoline engines, respectively; sulfate and nitrate for secondary aerosol formed with fossil and coal combustion and biogenic activity, respectively; Pb for combustion sources including biomass burning and two-stroke engine; Si for wind-blown soil; Ni and V for residual oil burning from ships and power plants; Cu and Zn for industrial emissions such as smelter effluents; and K for biomass burning [<a href="#B5-ijerph-16-02872" class="html-bibr">5</a>,<a href="#B14-ijerph-16-02872" class="html-bibr">14</a>,<a href="#B21-ijerph-16-02872" class="html-bibr">21</a>].</div></section><section id='sec2dot3-ijerph-16-02872' type=''><h4 class='html-italic' data-nested='2'> 2.3. Mortality</h4><div class='html-p'>Death certificate data from 2013 to 2015 were obtained from the Korea National Statistical Office. Nonaccidental, cardiovascular, and respiratory mortality were defined based on the International Classification of Diseases, 10-th revision code (World Health Organization, 2007): A00-R99, I00-I99, and J00-J99, respectively. Using these certificate data, we computed daily death counts for three types of mortality in each of the six major cities.</div></section><section id='sec2dot4-ijerph-16-02872' type=''><h4 class='html-italic' data-nested='2'> 2.4. Meteorology</h4><div class='html-p'>We obtained hourly measurements of temperature and relative humidity collected at a meteorology observation site in each of the six major cities from the Korea Meteorological Administration. Then, we computed daily average values of temperature (°C) and relative humidity (%).</div></section><section id='sec2dot5-ijerph-16-02872' type=''><h4 class='html-italic' data-nested='2'> 2.5. Statistical Analysis</h4><div class='html-p'>We used the generalized additive model (GAM) and fitted Poisson regression to examine short-term associations between the daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components and daily mortality adjusting for a long-term temporal trend, day of the week, and meteorology. We applied this model to each pair of the 12 pollutants and three types of mortality over all the cities and in each of the six cities. This model allows nonparametric smoothing functions to account for nonlinear relationships of time and temperature with daily death counts. The nonlinear trends of time and temperature were adjusted using thin plate regression splines [<a href="#B22-ijerph-16-02872" class="html-bibr">22</a>] with 18 (6 per year) and 3 degrees of freedom (df), respectively. To investigate the pattern of delayed effects of air pollution on mortality, we used pollutant concentrations from 0 to 7 days prior to death and estimated eight single-day lag effects (lag 0 to lag 7). In addition, we computed the moving averages for the previous 3–7 days (lag 0–3 and lag 0–7) to explore the impact of exposure over a few days or a week. The associations between daily concentrations of PM<sub>2.5</sub>/PM<sub>2.5</sub> components and daily mortality were presented as relative risks (RRs) for a city-specific interquartile range (IQR) increase in each pollutant’s concentration with respective 95% confidence intervals (CIs). We applied a city-specific IQR to allow the comparison of RRs across six cities, as the variability of each PM<sub>2.5</sub> component differs by cities. We also explored whether the PM<sub>2.5</sub> component with higher contribution to PM<sub>2.5</sub> shows stronger association with mortality of PM<sub>2.5</sub> or the corresponding PM<sub>2.5</sub> component. For this investigation, we looked at the relationship between a ratio of the average concentration of each PM<sub>2.5</sub> component to PM<sub>2.5</sub> concentrations and the RR of mortality for the component or PM<sub>2.5</sub> across six cities. </div><div class='html-p'>We performed two sensitivity analyses to investigate the sensitivity of our results to the modeling choice and data included. First, we changed the degree of adjustment for a long-term temporal trend using smaller or larger dfs (2 and 4 vs. 9 per year) than the primary choice (df = 6), and compared the results to the original findings. Secondly, we expanded our study period to all available years for 2011–2015 and compared to the primary results from 2013 to 2015. The available years varied by six cities: 2011–2015 for Seoul, Gwangju, and Daejeon; 2012–2015 for Daegu and Ulsan; and 2013–2015 for Busan. </div><div class='html-p'>We performed all statistical analyses for PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> components at their native scales. Statistical analyses were carried out in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 3.5.3 (The R Foundation for Statistical Computing, Vienna, Austria). This study was reviewed and approved by the Institutional Review Board of the Seoul National University (IRB No. E1905/002-001).</div></section></section><section id='sec3-ijerph-16-02872' type='results'><h2 data-nested='1'> 3. Results</h2><div class='html-p'><a href="#ijerph-16-02872-t001" class="html-table">Table 1</a> and <a href="#app1-ijerph-16-02872" class="html-app">Table S2</a> summarize the daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components, daily counts of nonaccidental, cardiovascular, and respiratory deaths, and daily temperature and humidity in each of the six major cities in South Korea, for 3 years from 2013 to 2015. The average daily PM<sub>2.5</sub> concentrations across the six cities ranged between 22.3 µg/m<sup>3</sup> in Ulsan and 27.9 µg/m<sup>3</sup> in Daejeon. The cities located in the central and western regions of South Korea including Seoul, Daejeon, and Gwangju showed high concentrations. The variability was also the highest in Seoul (standard deviation = 18.5 µg/m<sup>3</sup>). In contrast, average concentrations were relatively low in cities of the southeast regions (Busan, Daegu, and Ulsan), with small variability. This regional contrast was generally consistent for the PM<sub>2.5</sub> components except for OC and some metallic elements. Average daily concentrations of EC, sulfate, and nitrate were higher in Seoul, Daejeon, and Gwangju compared to the three southeastern cities. However, Ni and V were particularly high in Busan, which is the second largest city in South Korea, located on the southeast coast with large ports. Ulsan, another port city, showed the second largest concentrations of Ni and V, although concentrations of PM<sub>2.5</sub> and most components were the lowest. In addition, OC and Zn showed particularly high concentrations in Daegu compared to all the other cities. Daegu is located 36 km from Gumi, an industrial city for electronics, textiles, fibers, rubber, plastic, and metal products. </div><div class='html-p'>For seasonal variation, PM<sub>2.5</sub> showed higher concentrations in the winter and lower concentrations in the summer (<a href="#app1-ijerph-16-02872" class="html-app">Figure S1</a>). This seasonal contrast was generally consistent for PM<sub>2.5</sub> components across cities. However, Ni and V gave a reverse pattern with higher concentrations in the summer and lower concentrations in the winter. EC also showed winter peaks in Seoul but the pattern was inconsistent in Busan. <a href="#ijerph-16-02872-t002" class="html-table">Table 2</a> provides medians and ranges of correlation coefficients for pairs of PM<sub>2.5</sub> and PM<sub>2.5</sub> components across six cities. PM<sub>2.5</sub> was highly correlated with OC, nitrate, Pb, and K (median r = 0.72–0.76). Although the correlations of PM<sub>2.5</sub> with Ni and V were low (median r = 0.39–0.50), the correlation between the two components was high (0.75). While some components such as V showed similar correlations with PM<sub>2.5</sub> across cities, other components such as EC, OC, and nitrate showed large city-to-city variations in their correlations. </div><div class='html-p'>The average number of daily deaths was the highest in Seoul. Out of the 104 nonaccidental deaths per day, 22 and nine people died with cardiovascular and respiratory diseases, respectively. Over the six cities, cardiorespiratory mortality occupies one-fourth to one-third of nonaccidental mortality. Ulsan showed the lowest death counts, possibly because of a large young population compared to those in other cities (average age of 37.4 in Ulsan and 37.5–41.4 in the other five cities) [<a href="#B23-ijerph-16-02872" class="html-bibr">23</a>]. </div><div class='html-p'><a href="#ijerph-16-02872-f002" class="html-fig">Figure 2</a> shows RRs and 95% CIs of nonaccidental, cardiovascular, and respiratory mortality for IQR increases in PM<sub>2.5</sub> and each of the 11 PM<sub>2.5</sub> component concentrations across six cities. We present RRs at lag 1 for Seoul and lag 0 for all the other five cities. Our examination of multiple lags of exposure between 0 and 7 days are shown in <a href="#app1-ijerph-16-02872" class="html-app">Figure S2</a>. RRs of nonaccidental mortality for an IQR increase of PM<sub>2.5</sub> (16.6–21.7 µg/m<sup>3</sup>) ranged from 1.006 to 1.049 across all six cities, showing significant or marginal associations in three cities (Daegu: RR = 1.049, 95% CI = 1.016–1.083; Gwangju: 1.027, 0.998–1.058; and Daejeon: 1.045, 1.011–1.080). Of the three central and western cities that showed high concentrations of PM<sub>2.5</sub>, Gwangju and Daejeon also showed high RRs, whereas RR was relatively low in Seoul. Although PM<sub>2.5</sub> concentrations were low in Daegu, RR was higher for nonaccidental mortality compared to all the other cities. This pattern varies by cause-specific mortality. We found the association of PM<sub>2.5</sub> and cardiovascular mortality in Daegu (RR = 1.070, 95% CI = 1.014–1.128) and Ulsan (1.164, 1.056–1.284), whereas the association was found for respiratory mortality in Busan (1.077, 1.003–1.157) and Daejeon (1.192, 1.081–1.313). </div><div class='html-p'>For 11 PM<sub>2.5</sub> components and nonaccidental mortality, the patterns of RRs across six cities were similar to those for PM<sub>2.5</sub>. While the overall analysis for all the cities shows that RRs for all pollutants are marginally or significantly positive (<a href="#app1-ijerph-16-02872" class="html-app">Figure S3</a>), city-specific RRs are mostly positive with some variation depending on the components. RRs were generally higher in the central and western cities, Gwangju and Daejeon. Almost all PM<sub>2.5</sub> components showed significant associations in Daegu. Although there is large variation in the association across PM<sub>2.5</sub> components, EC was consistently associated with nonaccidental mortality in almost all the cities. There were particularly high and significant RRs for OC and V in Busan; Ni, sulfate, and K in Daegu; and nitrate in Daejeon, compared to those in the other cities. The association patterns for PM<sub>2.5</sub> components also varied by cause-specific mortality. For cardiovascular mortality, high RRs of nonaccidental mortality found in Daegu mostly remained. However, RRs in Gwangju and Daejeon were close to null, whereas high and significantly positive RRs were found for all components except OC and Si in Ulsan. The patterns for respiratory mortality were generally similar to those for nonaccidental mortality with increased RRs in Gwangju and Daejeon depending on the PM<sub>2.5</sub> components. In particular, we found increased RRs for Ni and V in Busan. In our analysis of the component contribution to PM<sub>2.5</sub> and RR, some PM<sub>2.5</sub> components with higher contributions to PM<sub>2.5</sub> showed higher RRs for both PM<sub>2.5</sub> and PM<sub>2.5</sub> components (<a href="#app1-ijerph-16-02872" class="html-app">Figures S4 and S5</a>). However, this pattern was not clear for all six cities and three types of mortality. In our sensitivity analysis, using greater or smaller df, the results were generally consistent with our primary results (<a href="#app1-ijerph-16-02872" class="html-app">Figure S6</a>). Including a few more years before 2013 depending on the city also gave consistent results (<a href="#app1-ijerph-16-02872" class="html-app">Figure S7</a>).</div></section><section id='sec4-ijerph-16-02872' type='discussion'><h2 data-nested='1'> 4. Discussion</h2><div class='html-p'>Using the monitoring data from the PM<sub>2.5</sub> chemical speciation networks for 3 years in six South Korean major cities, we found some heterogeneity in the short-term association between daily exposure to 11 PM<sub>2.5</sub> chemical components and daily mortality across major cities where different dominant pollution sources affect different chemical compositions of PM<sub>2.5</sub>. In general, we found higher concentrations of PM<sub>2.5</sub> components and stronger associations with mortality in cities on the west. However, in Daegu located near large industrial areas where some PM<sub>2.5</sub> component concentrations such as OC and Zn were notably high, the associations were generally stronger for all components compared to other cities. Ni and V were particularly high in Busan and Ulsan, as large port cities, and gave high mortality effect estimates. Unlike other components, EC showed marginal or significant associations with nonaccidental mortality across all six cities. The patterns also varied by cause-specific mortality. The associations of Ni and V were particularly strong with respiratory mortality in Busan. The association with cardiovascular mortality was strong for all PM<sub>2.5</sub> components in Ulsan, another large port city. </div><div class='html-p'>To our knowledge, this is the first Asian multicity study that compares the short-term associations between PM<sub>2.5</sub> components and mortality across major cities using nationwide regulatory monitoring data for PM<sub>2.5</sub> chemical speciation. All six cities were highly populated with more than one million people, and had relatively high pollution levels with annual average concentrations of PM<sub>2.5</sub> greater than 22 ug/m<sup>3</sup>. However, the urban environments of these cities vary distinctively, resulting in the variation of dominant sources from human activities such as traffic to heavy industries and large ports. A previous review study also indicates regional differences of source features based on individual source apportionment studies in each region of South Korea [<a href="#B24-ijerph-16-02872" class="html-bibr">24</a>]. This heterogeneity provides a good opportunity to investigate the similarities and differences in PM<sub>2.5</sub> component composition and related health effects. Our investigation based on Asian major cities can also help confirm existing findings of the association mostly based on studies in North America and Europe.</div><div class='html-p'>Our findings of strong associations for some PM<sub>2.5</sub> components related to specific pollution sources correspond to scientific understandings from previous studies. Many epidemiological studies suggest that the health effect of PM<sub>2.5</sub> is attributable to EC and some metallic elements [<a href="#B1-ijerph-16-02872" class="html-bibr">1</a>,<a href="#B3-ijerph-16-02872" class="html-bibr">3</a>,<a href="#B25-ijerph-16-02872" class="html-bibr">25</a>]. In particular, EC consistently shows association with mortality and morbidity, as concluded in a review of 25 cohort studies mostly performed in North America and Europe [<a href="#B26-ijerph-16-02872" class="html-bibr">26</a>]. As carbon itself is rarely toxic to humans, this indication may suggest various toxic substances related to carbon such as volatile organic compounds [<a href="#B27-ijerph-16-02872" class="html-bibr">27</a>]. In our study, the association of EC and mortality was consistent throughout all six cities. EC is considered a marker for diesel exhaust resulting from incomplete combustion, and may well represent high traffic congestion in major cities of South Korea as previous source apportionment studies suggested traffic as a major pollution source [<a href="#B24-ijerph-16-02872" class="html-bibr">24</a>]. Our finding of this association supports the adverse health effect of traffic-related air pollution reported in previous studies [<a href="#B8-ijerph-16-02872" class="html-bibr">8</a>]. Metallic elements are created mainly in industrial processes such as refining of metals, petroleum, and petrochemicals. Such components can be harmful once accumulated in the body, and have an influence that is proportional to their chemical composition, physical environments, and concentration levels [<a href="#B28-ijerph-16-02872" class="html-bibr">28</a>,<a href="#B29-ijerph-16-02872" class="html-bibr">29</a>,<a href="#B30-ijerph-16-02872" class="html-bibr">30</a>]. In this study, we found high concentrations of Ni and V, with strong associations to respiratory mortality in Busan, established as the largest port in South Korea. A previous study in the U.S. also found that high concentrations of Ni and V in New York City were responsible for the association of PM<sub>2.5</sub> to mortality in the U.S. [<a href="#B9-ijerph-16-02872" class="html-bibr">9</a>]. Ni has also been associated to mortality in Xian, China, known as high air pollution concentrations and combustion of fossil fuels such as coal and heavy oil [<a href="#B11-ijerph-16-02872" class="html-bibr">11</a>]. Toxicological findings also support our findings of Ni toxicity based on in vivo and in vitro studies [<a href="#B31-ijerph-16-02872" class="html-bibr">31</a>]. </div><div class='html-p'>The variability in 11 PM<sub>2.5</sub> component concentrations and in their associations with mortality found in our analysis across cities would explain the variability of the association between PM<sub>2.5</sub> and mortality (0.6 to 4.9% increase of RR for 16.6 to 21.7 ug/m<sup>3</sup> IQR increase of PM<sub>2.5</sub>), as reported in previous regional studies. For example, Busan and Ulsan with high ratios of Ni and V showed high effect estimates of PM<sub>2.5</sub> or those components. The different associations between PM<sub>2.5</sub> and mortality found in some studies in the U.S. and Europe were explained by different chemical composition of PM<sub>2.5</sub> [<a href="#B3-ijerph-16-02872" class="html-bibr">3</a>,<a href="#B4-ijerph-16-02872" class="html-bibr">4</a>,<a href="#B32-ijerph-16-02872" class="html-bibr">32</a>]. The meta-analysis of WHO regions showed substantial regional variation in the total mortality attributable to PM<sub>2.5</sub> with RR increase of 0.25 to 2.08%: 1.23% (95% CI: 0.45, 2.01), 0.94% (0.73, 1.16), and 0.25% (0.06, 0.44) in Europe, the U.S., and the Western Pacific, respectively, per 10 ug/m<sup>3</sup> increase in PM<sub>2.5</sub> [<a href="#B33-ijerph-16-02872" class="html-bibr">33</a>]. A recent meta-analysis study including 41 worldwide studies also reported large variability in risk estimates of total, cardiovascular, and respiratory mortality (RR and 95% CI per 10 ug/m<sup>3</sup> increment in PM<sub>2.5</sub> = 0.9, 0.7–1.1%; 0.8, 0.4–1.2%; 1.1, and 0.6–1.6%, respectively), with strong associations for EC and K [<a href="#B1-ijerph-16-02872" class="html-bibr">1</a>]. In region-specific studies, a meta-analysis conducted in six U.S. regions comprising 72 communities, showed that the association between PM<sub>2.5</sub> and total mortality could be attributed to EC, organic carbon matter, Si, and sodium ions [<a href="#B4-ijerph-16-02872" class="html-bibr">4</a>]. Another U.S. study, including 75 cities, also showed that total mortality was associated with Si, calcium, and sulfur [<a href="#B3-ijerph-16-02872" class="html-bibr">3</a>]. In southern Europe, including five countries, magnesium (Mg) and manganese were associated with cardiovascular mortality, whereas sulfate levels were associated with respiratory mortality [<a href="#B32-ijerph-16-02872" class="html-bibr">32</a>]. For morbidity, a U.S. study including 187 communities and 52 PM<sub>2.5</sub> components reported the associations of cardiovascular and respiratory hospital admissions with EC, Ni, and V [<a href="#B7-ijerph-16-02872" class="html-bibr">7</a>]. However, our study shows that high concentrations or proportions of PM<sub>2.5</sub> components to PM<sub>2.5</sub> do not necessarily provide strong associations with mortality over all cities. Some cities with high ratios of specific components, such as Ni and V, show high effect estimates of PM<sub>2.5</sub> or PM<sub>2.5</sub> components as shown in previous studies [<a href="#B5-ijerph-16-02872" class="html-bibr">5</a>,<a href="#B9-ijerph-16-02872" class="html-bibr">9</a>]. Other components and cities did not show consistent patterns. These findings suggest that spatially varying health effects could be due to the contribution of some specific components as well as other factors such as the composition of other components. </div><div class='html-p'>In Asia including South Korea, although there have been few multicity studies, some single-city time-series studies investigated PM<sub>2.5</sub> components and mortality. However, their findings were inconsistent. In Xian, one study reported the association between nitrate and total mortality [<a href="#B11-ijerph-16-02872" class="html-bibr">11</a>], whereas another study in the same city showed the association for EC, Pb, Zn, Ni, sulfate, ammonium, sulfur, and chorine, especially during the heating period [<a href="#B15-ijerph-16-02872" class="html-bibr">15</a>]. In Nagoya, Japan, sulfate was associated with total mortality in elderly people [<a href="#B16-ijerph-16-02872" class="html-bibr">16</a>]. The two previous studies performed in Seoul showed consistently positive associations between PM<sub>2.5</sub> and mortality, but findings also varied with respect to contributing components. Heo et al. (2014) [<a href="#B14-ijerph-16-02872" class="html-bibr">14</a>] collected PM<sub>2.5</sub> and components at a single sampling site near downtown from 2003 to 2007, and found that an IQR increment in PM<sub>2.5</sub> was associated with a 2.8% (95% CI: 0.2, 5.4) increase in cardiovascular mortality, which is higher than our RR of 0.6%. For PM<sub>2.5</sub> components, EC, OC, and Pb showed associations. Based on another sampling campaign at a site located in the eastern part of Seoul from 2008 to 2009, Son et al. (2012) [<a href="#B13-ijerph-16-02872" class="html-bibr">13</a>] also reported the association of PM<sub>2.5</sub>, but Mg was the only component associated with cardiovascular and total mortality. We also found positive RRs for PM<sub>2.5</sub>, EC, and OC in Seoul, but these were not statistically significant. This discrepancy in study results could be explained by different sampling periods and locations, and/or lab analysis methods [<a href="#B1-ijerph-16-02872" class="html-bibr">1</a>]. As the PM<sub>2.5</sub> speciation networks continue to measure PM<sub>2.5</sub> component concentrations, future studies adding monitoring data for extended years can help elucidate this inconsistency.</div><div class='html-p'>We found different associations by cause-specific mortality and lag pattern depending on the city. While there was a delayed pattern in Seoul with maximum effect estimates mostly at lag 1, all the other cities showed higher effect estimates at lag 0. This delayed pattern in Seoul is particularly distinct for respiratory mortality. RRs of respiratory mortality tended to be larger at longer lags and became positive from lag 4 to 6 for all PM<sub>2.5</sub> components. This pattern resulted in negative associations for most PM<sub>2.5</sub> components when the identical lag days (1 for Seoul and 0 for all the other cities) were applied to all three types of mortality. Effect estimates were high for cardiovascular mortality in Ulsan, whereas Daejeon and Busan showed high effect estimates for respiratory mortality. Some single-city studies in Denver and Atlanta, U.S., showed the patterns of immediate effects for cardiovascular diseases and delayed effects for respiratory diseases [<a href="#B6-ijerph-16-02872" class="html-bibr">6</a>,<a href="#B34-ijerph-16-02872" class="html-bibr">34</a>]. However, our multicity study shows that lag effects and cause-specific mortality effects are relatively homogeneous within a city and distinctively heterogeneous across cities. Prominent distinction in city characteristics including source features across different major cities could result in within-city homogeneity and between-city heterogeneity. The different associations in PM<sub>2.5</sub> components and mortality across cities could be due to different power or exposure measurement error. However, it is less likely that exposure measurement error driven by using measurements collected from different cities affect our results because we have utilized data with consistent sampling and lab protocols across the six cities.</div><div class='html-p'>Our study has several limitations to be addressed in future studies. First, we relied on PM<sub>2.5</sub> and PM<sub>2.5</sub> component monitoring data collected at one central monitoring site in each city. These measurements may not represent population levels of exposure, and could result in exposure misclassification and inaccurate health effect estimates particularly for some components known as their large variability at the fine spatial scale. However, when we compared our data to measurements at the second sites in Seoul and Ulsan, excluded in our analysis because of relatively short sampling, PM<sub>2.5</sub> component concentrations between the two sites were similar in their temporal patterns and were highly correlated (correlation coefficients of 0.96 and 0.76 in Seoul and Ulsan, respectively). These monitoring sites were also located at the top of public office buildings in highly populated areas without particular pollution sources, and can possibly represent population levels of exposure. In addition, this study was designed for assessing population-level relationships between PM<sub>2.5</sub> components and mortality. Future studies should confirm our finding based on monitoring campaigns that target individual-level exposures to PM<sub>2.5</sub> components. Lastly, future studies should investigate potential factors that further affect different associations between PM<sub>2.5</sub> or PM<sub>2.5</sub> components and health across cities in addition to PM<sub>2.5</sub> compositions. For example, housing conditions and/or air conditioning could affect residential infiltration and affect city-to-city heterogeneity of the association [<a href="#B35-ijerph-16-02872" class="html-bibr">35</a>].</div></section><section id='sec5-ijerph-16-02872' type='conclusions'><h2 data-nested='1'> 5. Conclusions</h2><div class='html-p'>Using regulatory monitoring data for PM<sub>2.5</sub> chemical components in six South Korean major cities, our study confirms the regional variation in the association between PM<sub>2.5</sub> components and mortality. This variation is relevant to the dominant pollution sources in each city. While PM<sub>2.5</sub> and EC showed marginal or significant associations over all cities, a heavily industrialized city showed the associations with most PM<sub>2.5</sub> components. Large port cities gave strong associations with shipping-related components such as Ni and V. </div></section> </div> <div class="html-back"> <section><section id='app1-ijerph-16-02872' type=''><h2 data-nested='1'> Supplementary Materials</h2><div class='html-p'>The following are available online at <a href='https://www.mdpi.com/1660-4601/16/16/2872/s1' target='_blank' rel="noopener noreferrer">https://www.mdpi.com/1660-4601/16/16/2872/s1</a>, Table S1. Average areas and populations of six South Korean major cities in 2015; Table S2. Interquartile range concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> components in six South Korean major cities from 2013 to 2015; Figure S1. Time-series plots of daily log-transformed concentrations of PM<sub>2.5</sub>, elemental carbon (EC), nickel (Ni), organic carbon (OC), copper (Cu), and vanadium (V), in the two largest South Korean cities (left: Seoul, right: Busan) from 2013 to 2015 (red lines for smoothed lines based on locally weighted scatterplot smoothing); Figure S2. Relative risks (RRs) and 95% confidence intervals of daily mortality for interquartile range increases in daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> components across seven single-day lags (lag 0 to lag 7) and two multiday lags (lag 0–3 and lag 0–7), in each of the six South Korean major cities from 2013 to 2015; Figure S3. Relative risks (RRs) and 95% confidence intervals of daily nonaccidental mortality for interquartile range increases in daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components over all six South Korean major cities from 2013 to 2015; Figure S4. Relative risks (RR)s and 95% confidence intervals (CIs) of mortality for PM<sub>2.5</sub> components against the ratios of PM<sub>2.5</sub> component concentrations to PM<sub>2.5</sub> concentrations across six South Korean major cities from 2013 to 2015 (ratio = 100 X PM<sub>2.5</sub> component concentration/PM<sub>2.5</sub> concentration; for PM<sub>2.5</sub>, RRs were plotted against concentrations); Figure S5. Relative risks (RR)s and 95% confidence intervals (CIs) of nonaccidental mortality for PM<sub>2.5</sub> against the ratios of PM<sub>2.5</sub> component concentrations to PM<sub>2.5</sub> concentrations across six South Korean major cities from 2013 to 2015 (ratio = 100 X PM<sub>2.5</sub> component concentration/PM<sub>2.5</sub> concentration; for PM<sub>2.5</sub>, RRs were plotted against concentrations); Figure S6. Relative risks (RRs) and 95% confidence intervals of nonaccidental mortality for 11 PM<sub>2.5</sub> components by different degrees of freedom (df) used for temporal trend adjustment (our primary model with df 6 per year); Figure S7. Relative risks (RRs) and 95% confidence intervals of daily mortality for interquartile range increases in daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> components for all available years between 2011 and 2015.</div></section></section><section class='html-notes'><h2 >Author Contributions</h2><div class='html-p'>S.-E.Y., designed the study, performed data analyses, and wrote the manuscript; J.-S.P. and S.-B.L., provided data; S.H.L., contributed to data analysis and interpretation; C.-H.P., C.-W.L., and S.D.Y., contributed to interpretation of analysis results; S.-Y.K., advised data analysis and result interpretation, and contributed to manuscript writing; and H.K., advised interpretation of results.</div></section><section class='html-notes'><h2 >Funding</h2><div class='html-p'>This research was supported by grants from the National Institute of Environment Research (NIER) funded by the Ministry of Environment (MOE) of the Republic of Korea [NIER-2018-01-01-002, NIER-2018-03-01-003]. Additional support was provided by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [2018R1A2B6004608) and the National Cancer Center of Korea [NCC-1810220-01].</div></section><section class='html-notes'><h2 >Conflicts of Interest</h2><div class='html-p'>The authors declare that there are no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. </div></section><section id='html-references_list'><h2>References</h2><ol class='html-xx'><li id='B1-ijerph-16-02872' class='html-x' data-content='1.'>Acilleos, S.; Kiomourtzoglu, M.A.; Wu, C.D.; Schwarts, J.D.; Koutrakis, P.; Papatheodorou, S.I. Acute effects of fine particulate matter constituents on mortality: A systemic review and meta-regression. <span class='html-italic'>Environ. Int.</span> <b>2017</b>, <span class='html-italic'>109</span>, 89–100. 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data-lsrc="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g001-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/1660-4601/16/16/2872/display" href="#fig_body_display_ijerph-16-02872-f001"></a> </div> </div> <div class="html-fig_description"> <b>Figure 1.</b> Map of the six major cities (Seoul, Daejeon, Gwangju, Daegu, Ulsan, and Busan) and six PM<sub>2.5</sub> speciation network monitoring sites in South Korea (nine provinces in black: Gyeonggi, Gangwon, Chungbuk, Chungnam, Gyeongbuk, Gyeongnam, Jeonbuk, Jeonnam, and Jeju).  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijerph-16-02872-f001" > <div class="html-caption" > <b>Figure 1.</b> Map of the six major cities (Seoul, Daejeon, Gwangju, Daegu, Ulsan, and Busan) and six PM<sub>2.5</sub> speciation network monitoring sites in South Korea (nine provinces in black: Gyeonggi, Gangwon, Chungbuk, Chungnam, Gyeongbuk, Gyeongnam, Jeonbuk, Jeonnam, and Jeju).</div> <div class="html-img"><img alt="Ijerph 16 02872 g001" data-large="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g001.png" data-original="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g001.png" onerror="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g001.png" data-lsrc="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g001.png" /></div> </div><div class="html-fig-wrap" id="ijerph-16-02872-f002"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/1660-4601/16/16/2872/display" href="#fig_body_display_ijerph-16-02872-f002"> <img alt="Ijerph 16 02872 g002 550" data-large="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png" data-original="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png" onerror="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png" data-lsrc="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/1660-4601/16/16/2872/display" href="#fig_body_display_ijerph-16-02872-f002"></a> </div> </div> <div class="html-fig_description"> <b>Figure 2.</b> Relative risks (RRs) and 95% confidence intervals of daily nonaccidental, cardiovascular and respiratory mortality for interquartile range increases (IQR) in daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components across the six South Korean major cities from 2013 to 2015.  </div> </div> <div class="html-fig_show mfp-hide" id ="fig_body_display_ijerph-16-02872-f002" > <div class="html-caption" > <b>Figure 2.</b> Relative risks (RRs) and 95% confidence intervals of daily nonaccidental, cardiovascular and respiratory mortality for interquartile range increases (IQR) in daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components across the six South Korean major cities from 2013 to 2015.</div> <div class="html-img"><img alt="Ijerph 16 02872 g002" data-large="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png" data-original="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png" onerror="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png" data-lsrc="/ijerph/ijerph-16-02872/article_deploy/html/images/ijerph-16-02872-g002.png" /></div> </div><div class="html-table-wrap" id="ijerph-16-02872-t001"> <div class="html-table_wrap_td" > <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/1660-4601/16/16/2872/display" href='#table_body_display_ijerph-16-02872-t001'> <img alt="Table" data-lsrc="https://www.mdpi.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/1660-4601/16/16/2872/display" href="#table_body_display_ijerph-16-02872-t001"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 1.</b> Means and standard deviation of daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components, and means of daily mortality and weather variables in each of the six South Korean major cities for the period from 2013 to 2015. </div> </div> <div class="html-table_show mfp-hide " id ="table_body_display_ijerph-16-02872-t001" > <div class="html-caption" ><b>Table 1.</b> Means and standard deviation of daily concentrations of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components, and means of daily mortality and weather variables in each of the six South Korean major cities for the period from 2013 to 2015.</div> <table > <thead ><tr ><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >City</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >N</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >PM<sub>2.5</sub> (µg/m<sup>3</sup>)</th><th colspan='11' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >PM<sub>2.5</sub> Components</th><th colspan='3' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Mortality</th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Meteorology</th></tr><tr ><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >EC (µg/m)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >OC <sub>(</sub>µg/m<sup>3</sup><sub>)</sub></th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >NO<sub>3</sub><sup>−</sup> (µg/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >SO<sub>4</sub><sup>2−</sup> (µg/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Pb <br>(ng/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Ni (ng/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Si <br>(ng/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >V <br>(ng/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Cu (ng/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Zn <br>(ng/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >K <br>(ng/m<sup>3</sup>)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Non-<br>Accidental</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >CVD</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >RD</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Temp (°C)</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >RH (%)</th></tr></thead><tbody ><tr ><td align='center' valign='middle' class='html-align-center' >Seoul</td><td align='center' valign='middle' class='html-align-center' >517</td><td align='center' valign='middle' class='html-align-center' >27.1 (18.5)</td><td align='center' valign='middle' class='html-align-center' >1.9 (0.9)</td><td align='center' valign='middle' class='html-align-center' >4.8 (2.3)</td><td align='center' valign='middle' class='html-align-center' >4.7 (5.8)</td><td align='center' valign='middle' class='html-align-center' >6.9 (5.7)</td><td align='center' valign='middle' class='html-align-center' >28.8 (29.1)</td><td align='center' valign='middle' class='html-align-center' >2.2 (2.1)</td><td align='center' valign='middle' class='html-align-center' >768.3 (629.4)</td><td align='center' valign='middle' class='html-align-center' >5.7 (5.8)</td><td align='center' valign='middle' class='html-align-center' >7.9 (7.1)</td><td align='center' valign='middle' class='html-align-center' >67.9 (54.1)</td><td align='center' valign='middle' class='html-align-center' >349.8 (264.7) </td><td align='center' valign='middle' class='html-align-center' >104.2 </td><td align='center' valign='middle' class='html-align-center' >21.9</td><td align='center' valign='middle' class='html-align-center' >9.2 </td><td align='center' valign='middle' class='html-align-center' >13.2</td><td align='center' valign='middle' class='html-align-center' >60.9</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Busan</td><td align='center' valign='middle' class='html-align-center' >283</td><td align='center' valign='middle' class='html-align-center' >23.7 (13.1)</td><td align='center' valign='middle' class='html-align-center' >0.8 (0.6)</td><td align='center' valign='middle' class='html-align-center' >4.7 (2.3)</td><td align='center' valign='middle' class='html-align-center' >2.4 (3.3)</td><td align='center' valign='middle' class='html-align-center' >6.1 (4.8)</td><td align='center' valign='middle' class='html-align-center' >24.0 (17.3)</td><td align='center' valign='middle' class='html-align-center' >3.9 (3.0)</td><td align='center' valign='middle' class='html-align-center' >565.1 (532.5) </td><td align='center' valign='middle' class='html-align-center' >8.5 (8.9)</td><td align='center' valign='middle' class='html-align-center' >7.5 (5.5)</td><td align='center' valign='middle' class='html-align-center' >78.4 (49.0)</td><td align='center' valign='middle' class='html-align-center' >255.8 (171.6)</td><td align='center' valign='middle' class='html-align-center' >50.3</td><td align='center' valign='middle' class='html-align-center' >14.0 </td><td align='center' valign='middle' class='html-align-center' >5.0 </td><td align='center' valign='middle' class='html-align-center' >15.3</td><td align='center' valign='middle' class='html-align-center' >63.5</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Daegu</td><td align='center' valign='middle' class='html-align-center' >286</td><td align='center' valign='middle' class='html-align-center' >23.4 (14.0)</td><td align='center' valign='middle' class='html-align-center' >0.8 (0.9)</td><td align='center' valign='middle' class='html-align-center' >5.0 (3.1)</td><td align='center' valign='middle' class='html-align-center' >2.6 (3.7)</td><td align='center' valign='middle' class='html-align-center' >4.8 (3.4) </td><td align='center' valign='middle' class='html-align-center' >22.7 (17.5) </td><td align='center' valign='middle' class='html-align-center' >2.0 (1.4)</td><td align='center' valign='middle' class='html-align-center' >564.1 (488.4)</td><td align='center' valign='middle' class='html-align-center' >2.6 (2.4)</td><td align='center' valign='middle' class='html-align-center' >7.1 (7.1)</td><td align='center' valign='middle' class='html-align-center' >134.9 (138.1)</td><td align='center' valign='middle' class='html-align-center' >261.3 (182.5)</td><td align='center' valign='middle' class='html-align-center' >31.4</td><td align='center' valign='middle' class='html-align-center' >8.7 </td><td align='center' valign='middle' class='html-align-center' >3.4 </td><td align='center' valign='middle' class='html-align-center' >15.0 </td><td align='center' valign='middle' class='html-align-center' >59.3 </td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Gwangju</td><td align='center' valign='middle' class='html-align-center' >486</td><td align='center' valign='middle' class='html-align-center' >26.0 (16.5)</td><td align='center' valign='middle' class='html-align-center' >1.8 (1.1) </td><td align='center' valign='middle' class='html-align-center' >4.5 (1.8)</td><td align='center' valign='middle' class='html-align-center' >3.4 (4.9)</td><td align='center' valign='middle' class='html-align-center' >6.7 (5.6) </td><td align='center' valign='middle' class='html-align-center' >28.9 (24.8) </td><td align='center' valign='middle' class='html-align-center' >1.9 (2.0) </td><td align='center' valign='middle' class='html-align-center' >801.7 (682.0)</td><td align='center' valign='middle' class='html-align-center' >4.1 (3.9) </td><td align='center' valign='middle' class='html-align-center' >6.1 (8.1)</td><td align='center' valign='middle' class='html-align-center' >69.2 (45.0)</td><td align='center' valign='middle' class='html-align-center' >352.3 (268.5)</td><td align='center' valign='middle' class='html-align-center' >17.3</td><td align='center' valign='middle' class='html-align-center' >4.1</td><td align='center' valign='middle' class='html-align-center' >2.2 </td><td align='center' valign='middle' class='html-align-center' >14.4</td><td align='center' valign='middle' class='html-align-center' >66.7 </td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Daejeon</td><td align='center' valign='middle' class='html-align-center' >437</td><td align='center' valign='middle' class='html-align-center' >27.9 (17.0)</td><td align='center' valign='middle' class='html-align-center' >2.2 (1.0) </td><td align='center' valign='middle' class='html-align-center' >4.7 (1.8)</td><td align='center' valign='middle' class='html-align-center' >4.5 (5.9) </td><td align='center' valign='middle' class='html-align-center' >6.7 (5.1) </td><td align='center' valign='middle' class='html-align-center' >27.5 (24.5)</td><td align='center' valign='middle' class='html-align-center' >2.1 (2.3)</td><td align='center' valign='middle' class='html-align-center' >744.8 (766.2)</td><td align='center' valign='middle' class='html-align-center' >3.6 (3.1) </td><td align='center' valign='middle' class='html-align-center' >6.1 (5.1)</td><td align='center' valign='middle' class='html-align-center' >64.4 (44.2)</td><td align='center' valign='middle' class='html-align-center' >328.8 (251.1)</td><td align='center' valign='middle' class='html-align-center' >16.2</td><td align='center' valign='middle' class='html-align-center' >3.6 </td><td align='center' valign='middle' class='html-align-center' >2.0</td><td align='center' valign='middle' class='html-align-center' >13.5 </td><td align='center' valign='middle' class='html-align-center' >71.7 </td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Ulsan</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >247</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >22.3 (12.9)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.7 (0.6)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >3.8 (2.1) </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.8 (2.7)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >5.6 (4.5)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >21.9 (21.2)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.8 (2.7) </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >646.2 (676.3)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >6.3 (8.0) </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >5.7 (5.5)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >65.1 (50.6) </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >224.7 (163.5)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >11.6 </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >3.3</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.7</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >14.9</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >63.1 </td></tr></tbody> </table> <div class='html-table_foot html-p'><div class='html-p' style='text-indent:0em;'><span class='html-fn-content'>Abbreviation: EC = elemental carbon; OC = organic carbon; NO<sub>3</sub><sup>−</sup> = nitrate; SO<sub>4</sub><sup>2−</sup> = sulfate; Pb = lead; Ni = nickel; Si = silicon; V = vanadium; Cu = copper, Zn = zinc, K = potassium; CVD = cardiovascular disease; RD = respiratory disease; Temp = temperature; RH = relative humidity. Six cities ordered by population proportion (city population/national population) in 2015: 19.4, 6.8, 4.8, 2.9, 2.9, and 2.3% for Seoul, Busan, Daegu, Gwangju, Daejeon, and Ulsan, respectively.</span></div><div style='clear:both;'></div></div> </div><div class="html-table-wrap" id="ijerph-16-02872-t002"> <div class="html-table_wrap_td" > <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/1660-4601/16/16/2872/display" href='#table_body_display_ijerph-16-02872-t002'> <img alt="Table" data-lsrc="https://www.mdpi.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/1660-4601/16/16/2872/display" href="#table_body_display_ijerph-16-02872-t002"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 2.</b> Medians and ranges of correlation coefficients of pairs of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components across six South Korean major cities from 2013 to 2015. </div> </div> <div class="html-table_show mfp-hide " id ="table_body_display_ijerph-16-02872-t002" > <div class="html-caption" ><b>Table 2.</b> Medians and ranges of correlation coefficients of pairs of PM<sub>2.5</sub> and 11 PM<sub>2.5</sub> chemical components across six South Korean major cities from 2013 to 2015.</div> <table > <thead ><tr ><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' > </th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >EC</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >OC</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >NO<sub>3</sub><sup>−</sup></th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >SO<sub>4</sub><sup>2−</sup></th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Pb</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Ni</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Si</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >V</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Cu</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Zn</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >K</th></tr></thead><tbody ><tr ><td align='center' valign='middle' class='html-align-center' >PM<sub>2.5</sub></td><td align='center' valign='middle' class='html-align-center' >0.58 (0.41–0.78)</td><td align='center' valign='middle' class='html-align-center' >0.72 (0.48–0.83)</td><td align='center' valign='middle' class='html-align-center' >0.76 (0.49–0.85)</td><td align='center' valign='middle' class='html-align-center' >0.68 (0.57–0.74)</td><td align='center' valign='middle' class='html-align-center' >0.72 (0.61–0.81)</td><td align='center' valign='middle' class='html-align-center' >0.50 (0.34–0.61)</td><td align='center' valign='middle' class='html-align-center' >0.52 (0.41–0.62)</td><td align='center' valign='middle' class='html-align-center' >0.39 (0.33–0.48)</td><td align='center' valign='middle' class='html-align-center' >0.57 (0.33–0.62)</td><td align='center' valign='middle' class='html-align-center' >0.68 (0.56–0.76)</td><td align='center' valign='middle' class='html-align-center' >0.76 (0.63–0.81)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >EC</td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.62 (0.08–0.77)</td><td align='center' valign='middle' class='html-align-center' >0.46 (0.25–0.70)</td><td align='center' valign='middle' class='html-align-center' >0.34 (0.27–0.40)</td><td align='center' valign='middle' class='html-align-center' >0.46 (0.27–0.61)</td><td align='center' valign='middle' class='html-align-center' >0.23 (0.10–0.31)</td><td align='center' valign='middle' class='html-align-center' >0.19 (0.04–0.42)</td><td align='center' valign='middle' class='html-align-center' >0.15 (0.01–0.19)</td><td align='center' valign='middle' class='html-align-center' >0.36 (0.21–0.49)</td><td align='center' valign='middle' class='html-align-center' >0.42 (0.29–0.58)</td><td align='center' valign='middle' class='html-align-center' >0.49 (0.25–0.67)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >OC</td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.60 (0.39–0.69)</td><td align='center' valign='middle' class='html-align-center' >0.37 (0.29–0.53)</td><td align='center' valign='middle' class='html-align-center' >0.55 (0.31–0.68)</td><td align='center' valign='middle' class='html-align-center' >0.35 (0.22–0.49)</td><td align='center' valign='middle' class='html-align-center' >0.38 (0.34–0.45)</td><td align='center' valign='middle' class='html-align-center' >0.25 (0.13–0.34)</td><td align='center' valign='middle' class='html-align-center' >0.45 (0.28–0.53)</td><td align='center' valign='middle' class='html-align-center' >0.54 (0.46–0.65)</td><td align='center' valign='middle' class='html-align-center' >0.63 (0.39–0.71)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >NO<sub>3</sub><sup>−</sup></td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.32 (0.20–0.42)</td><td align='center' valign='middle' class='html-align-center' >0.59 (0.41–0.72)</td><td align='center' valign='middle' class='html-align-center' >0.24 (0.08–0.42)</td><td align='center' valign='middle' class='html-align-center' >0.27 (0.13–0.34)</td><td align='center' valign='middle' class='html-align-center' >0.07 (−0.14–0.28)</td><td align='center' valign='middle' class='html-align-center' >0.52 (0.26–0.56)</td><td align='center' valign='middle' class='html-align-center' >0.50 (0.35–0.59)</td><td align='center' valign='middle' class='html-align-center' >0.62 (0.52–0.67)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >SO<sub>4</sub><sup>2−</sup></td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.50 (0.42–0.57)</td><td align='center' valign='middle' class='html-align-center' >0.46 (0.24–0.52)</td><td align='center' valign='middle' class='html-align-center' >0.37 (0.32–0.44)</td><td align='center' valign='middle' class='html-align-center' >0.48 (0.36–0.53)</td><td align='center' valign='middle' class='html-align-center' >0.32 (0.15–0.44)</td><td align='center' valign='middle' class='html-align-center' >0.48 (0.27–0.55)</td><td align='center' valign='middle' class='html-align-center' >0.46 (0.44–0.49)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Pb</td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.46 (0.30–0.62)</td><td align='center' valign='middle' class='html-align-center' >0.47 (0.36–0.51)</td><td align='center' valign='middle' class='html-align-center' >0.35 (0.11–0.54)</td><td align='center' valign='middle' class='html-align-center' >0.70 (0.46–0.85)</td><td align='center' valign='middle' class='html-align-center' >0.72 (0.58–0.87)</td><td align='center' valign='middle' class='html-align-center' >0.72 (0.57–0.81)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Ni</td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.36 (0.20–0.45)</td><td align='center' valign='middle' class='html-align-center' >0.75 (0.46–0.91)</td><td align='center' valign='middle' class='html-align-center' >0.58 (0.40–0.72)</td><td align='center' valign='middle' class='html-align-center' >0.61 (0.41–0.70)</td><td align='center' valign='middle' class='html-align-center' >0.38 (0.24–0.48)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Si</td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.44 (0.23–0.50)</td><td align='center' valign='middle' class='html-align-center' >0.39 (0.25–0.47)</td><td align='center' valign='middle' class='html-align-center' >0.48 (0.34–0.58)</td><td align='center' valign='middle' class='html-align-center' >0.67 (0.62–0.74)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >V</td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.43 (−0.02–0.57)</td><td align='center' valign='middle' class='html-align-center' >0.49 (0.11–0.59)</td><td align='center' valign='middle' class='html-align-center' >0.32 (−0.01–0.45)</td></tr><tr ><td align='center' valign='middle' class='html-align-center' >Cu</td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' > </td><td align='center' valign='middle' class='html-align-center' >0.76 (0.46–0.88)</td><td align='center' valign='middle' class='html-align-center' >0.56 (0.42–0.66)</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Zn</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' > </td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.65 (0.52–0.74)</td></tr></tbody> </table> </div></section> <section id="html-copyright"><br>© 2019 by the authors. 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Comparison of Short-Term Associations between PM<sub>2.5</sub> Components and Mortality across Six Major Cities in South Korea. <em>Int. J. Environ. Res. Public Health</em> <b>2019</b>, <em>16</em>, 2872. https://doi.org/10.3390/ijerph16162872 </p> <div style="display: block"> <b>AMA Style</b><br> <p> Yoo S-E, Park J-S, Lee SH, Park C-H, Lee C-W, Lee S-B, Yu SD, Kim S-Y, Kim H. Comparison of Short-Term Associations between PM<sub>2.5</sub> Components and Mortality across Six Major Cities in South Korea. <em>International Journal of Environmental Research and Public Health</em>. 2019; 16(16):2872. https://doi.org/10.3390/ijerph16162872 </p> <b>Chicago/Turabian Style</b><br> <p> Yoo, Si-Eun, Jin-Soo Park, Soo Hyun Lee, Choong-Hee Park, Chul-Woo Lee, Sang-Bo Lee, Seung Do Yu, Sun-Young Kim, and Ho Kim. 2019. "Comparison of Short-Term Associations between PM<sub>2.5</sub> Components and Mortality across Six Major Cities in South Korea" <em>International Journal of Environmental Research and Public Health</em> 16, no. 16: 2872. https://doi.org/10.3390/ijerph16162872 </p> <b>APA Style</b><br> <p> Yoo, S. -E., Park, J. -S., Lee, S. H., Park, C. -H., Lee, C. -W., Lee, S. -B., Yu, S. D., Kim, S. -Y., & Kim, H. (2019). Comparison of Short-Term Associations between PM<sub>2.5</sub> Components and Mortality across Six Major Cities in South Korea. <em>International Journal of Environmental Research and Public Health</em>, <em>16</em>(16), 2872. https://doi.org/10.3390/ijerph16162872 </p> </div> </div> <div class="info-box no-margin"> Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. 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