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data-trace="false" data-dom-id="ProfileCheckPaperUpdate-react-component-75c89d98-092b-4e25-8c5e-bf4592b37851"></div> <div id="ProfileCheckPaperUpdate-react-component-75c89d98-092b-4e25-8c5e-bf4592b37851"></div> <div class="DesignSystem"><div class="onsite-ping" id="onsite-ping"></div></div><div class="profile-user-info DesignSystem"><div class="social-profile-container"><div class="left-panel-container"><div class="user-info-component-wrapper"><div class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Michal Korek</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://ki.academia.edu/">Karolinska Institutet</a>, <a class="u-tcGrayDarker" 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id="Pill-react-component-be714dee-6022-4135-b553-6662f5f64297"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Michal Korek</h3></div><div class="js-work-strip profile--work_container" data-work-id="13683434"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/13683434/Traffic_related_air_pollution_exposure_and_incidence_of_stroke_in_four_cohorts_from_Stockholm"><img alt="Research paper thumbnail of Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13683434/Traffic_related_air_pollution_exposure_and_incidence_of_stroke_in_four_cohorts_from_Stockholm">Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm</a></div><div class="wp-workCard_item"><span>Journal of exposure science & environmental epidemiology</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigated the risk of stroke related to long-term ambient air pollution exposure, in partic...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigated the risk of stroke related to long-term ambient air pollution exposure, in particular the role of various exposure time windows, using four cohorts from Stockholm County, Sweden. In total, 22,587 individuals were recruited from 1992 to 2004 and followed until 2011. Yearly air pollution levels resulting from local road traffic emissions were assessed at participant residences using dispersion models for particulate matter (PM10) and nitrogen oxides (NOX). Cohort-specific hazard ratios were estimated for time-weighted air pollution exposure during different time windows and the incidence of stroke, adjusted for common risk factors, and then meta-analysed. Overall, 868 subjects suffered a non-fatal or fatal stroke during 238,731 person-years of follow-up. An increment of 20 μg/m(3) in estimated annual mean of road-traffic related NOX exposure at recruitment was associated with a hazard ratio of 1.16 (95% CI 0.83-1.61), with evidence of heterogeneity between the cohorts....</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13683434"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13683434"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13683434; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13683434]").text(description); $(".js-view-count[data-work-id=13683434]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13683434; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13683434']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13683434, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13683434]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13683434,"title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm","translated_title":"","metadata":{"abstract":"We investigated the risk of stroke related to long-term ambient air pollution exposure, in particular the role of various exposure time windows, using four cohorts from Stockholm County, Sweden. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="13683433"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/13683433/Arterial_Blood_Pressure_and_Long_Term_Exposure_to_Traffic_Related_Air_Pollution_An_Analysis_in_the_European_Study_of_Cohorts_for_Air_Pollution_Effects_ESCAPE_"><img alt="Research paper thumbnail of Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13683433/Arterial_Blood_Pressure_and_Long_Term_Exposure_to_Traffic_Related_Air_Pollution_An_Analysis_in_the_European_Study_of_Cohorts_for_Air_Pollution_Effects_ESCAPE_">Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)</a></div><div class="wp-workCard_item"><span>Environmental Health Perspectives</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP)...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific. We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations. We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis. In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated. In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. The relationship of modeled air pollutants with BP was inconsistent.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13683433"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13683433"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13683433; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13683433]").text(description); $(".js-view-count[data-work-id=13683433]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13683433; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13683433']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13683433, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13683433]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13683433,"title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)","translated_title":"","metadata":{"abstract":"Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific. We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations. We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis. In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated. In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. The relationship of modeled air pollutants with BP was inconsistent.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Environmental Health Perspectives"},"translated_abstract":"Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific. We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations. We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis. In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated. In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. The relationship of modeled air pollutants with BP was inconsistent.","internal_url":"https://www.academia.edu/13683433/Arterial_Blood_Pressure_and_Long_Term_Exposure_to_Traffic_Related_Air_Pollution_An_Analysis_in_the_European_Study_of_Cohorts_for_Air_Pollution_Effects_ESCAPE_","translated_internal_url":"","created_at":"2015-07-06T00:24:42.024-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32826726,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":2378652,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":135499,"email":"m***n@imim.es","display_order":0,"name":"Mark Nieuwenhuijsen","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378656,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":135498,"email":"m***n@creal.cat","display_order":4194304,"name":"Mark Nieuwenhuijsen","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378658,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":115595,"email":"p***s@helmholtz-muenchen.de","display_order":6291456,"name":"Annette Peters","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378662,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":31999659,"co_author_invite_id":null,"email":"n***n@ki.se","display_order":7340032,"name":"Nancy Pedersen","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378665,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":284101,"email":"t***i@unibas.ch","display_order":7864320,"name":"Tamara Schikowski","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378666,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":284102,"email":"t***i@iuf-duesseldorf.de","display_order":8126464,"name":"Tamara Schikowski","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378680,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":284112,"email":"b***f@vet.uu.nl","display_order":8323072,"name":"Bert Brunekreef","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378699,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":38513023,"co_author_invite_id":284115,"email":"m***i@unibas.ch","affiliation":"Swiss Tropical and Public Health Institute (Swiss TPH)","display_order":8355840,"name":"Ming-Yi Tsai","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378706,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":284108,"email":"b***n@iuf-duesseldorf.de","display_order":8372224,"name":"Barbara Hoffmann","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378710,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":32505549,"co_author_invite_id":null,"email":"m***s@uu.nl","affiliation":"Swiss Tropical and Public Health Institute (Swiss TPH)","display_order":8380416,"name":"Marloes Eeftens","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378732,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":284113,"email":"r***n@uu.nl","display_order":8384512,"name":"R. Beelen","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378734,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":32320535,"co_author_invite_id":null,"email":"m***s@umcutrecht.nl","display_order":8386560,"name":"Michiel Bots","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378736,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":35007401,"co_author_invite_id":284054,"email":"k***r@ki.se","display_order":8387584,"name":"Karin Leander","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378751,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":305575,"email":"k***u@uni-duesseldorf.de","display_order":8388096,"name":"Ursula Krämer","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378752,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":305576,"email":"i***a@imim.es","display_order":8388352,"name":"Inmaculada Aguilera","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378767,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":272046,"email":"p***n@ki.se","display_order":8388480,"name":"Patrik Magnusson","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378776,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":284109,"email":"g***n@ki.se","display_order":8388544,"name":"Göran Pershagen","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"},{"id":2378789,"work_id":13683433,"tagging_user_id":32826726,"tagged_user_id":32100585,"co_author_invite_id":null,"email":"a***e@imperial.ac.uk","display_order":8388576,"name":"Audrey Nazelle","title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)"}],"downloadable_attachments":[],"slug":"Arterial_Blood_Pressure_and_Long_Term_Exposure_to_Traffic_Related_Air_Pollution_An_Analysis_in_the_European_Study_of_Cohorts_for_Air_Pollution_Effects_ESCAPE_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32826726,"first_name":"Michal","middle_initials":null,"last_name":"Korek","page_name":"MichalKorek","domain_name":"ki","created_at":"2015-07-06T00:23:47.506-07:00","display_name":"Michal Korek","url":"https://ki.academia.edu/MichalKorek"},"attachments":[],"research_interests":[{"id":58054,"name":"Environmental Sciences","url":"https://www.academia.edu/Documents/in/Environmental_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12890199"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12890199/Comparing_land_use_regression_and_dispersion_modelling_to_assess_residential_exposure_to_ambient_air_pollution_for_epidemiological_studies"><img alt="Research paper thumbnail of Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies" class="work-thumbnail" src="https://attachments.academia-assets.com/60076889/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12890199/Comparing_land_use_regression_and_dispersion_modelling_to_assess_residential_exposure_to_ambient_air_pollution_for_epidemiological_studies">Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ChiaraBadaloni">Chiara Badaloni</a></span></div><div class="wp-workCard_item"><span>Environment International</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec25da2d01f0ea27717e99b5095b2830" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60076889,"asset_id":12890199,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60076889/download_file?st=MTczMzAxMTI3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12890199"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12890199"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12890199; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12890199]").text(description); $(".js-view-count[data-work-id=12890199]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12890199; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12890199']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12890199, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ec25da2d01f0ea27717e99b5095b2830" } } $('.js-work-strip[data-work-id=12890199]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12890199,"title":"Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies","translated_title":"","metadata":{"abstract":"Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.","ai_title_tag":"Comparing Land Use Regression and Dispersion Models for Air Quality","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Environment International"},"translated_abstract":"Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.","internal_url":"https://www.academia.edu/12890199/Comparing_land_use_regression_and_dispersion_modelling_to_assess_residential_exposure_to_ambient_air_pollution_for_epidemiological_studies","translated_internal_url":"","created_at":"2015-06-09T12:07:01.945-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32035136,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":825357,"work_id":12890199,"tagging_user_id":32035136,"tagged_user_id":38513023,"co_author_invite_id":284115,"email":"m***i@unibas.ch","affiliation":"Swiss Tropical and Public Health Institute (Swiss TPH)","display_order":-7717518,"name":"Ming-Yi Tsai","title":"Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological 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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/13683432/Performance_of_multi_city_land_use_regression_models_for_nitrogen_dioxide_and_fine_particles"><img alt="Research paper thumbnail of Performance of multi-city land use regression models for nitrogen dioxide and fine particles" class="work-thumbnail" src="https://attachments.academia-assets.com/45063866/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/13683432/Performance_of_multi_city_land_use_regression_models_for_nitrogen_dioxide_and_fine_particles">Performance of multi-city land use regression models for nitrogen dioxide and fine particles</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="13683431"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/13683431/A_multicentre_study_of_air_pollution_exposure_and_childhood_asthma_prevalence_the_ESCAPE_project"><img alt="Research paper thumbnail of A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13683431/A_multicentre_study_of_air_pollution_exposure_and_childhood_asthma_prevalence_the_ESCAPE_project">A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnaM%C3%B6lter">Anna Mölter</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The aim of this study was to determine the effect of six traffic-related air pollution metrics (n...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;10 μm (PM10), PM2.5, coarse particulate matter and PM2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, divided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4-5 and 8-10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from individual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure (e.g. adjusted OR (95%CI) for asthma at age 8-10 years and exposure at the birth address (n=10377): 1.10 (0.81-1.49) per 10 μg·m(-3) nitrogen dioxide; 0.88 (0.63-1.24) per 10 μg·m(-3) PM10; 1.23 (0.78-1.95) per 5 μg·m(-3) PM2.5). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. 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with lung function" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13012358/Elemental_composition_of_particulate_matter_and_the_association_with_lung_function">Elemental composition of particulate matter and the association with lung function</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AleksandraJedynska">Aleksandra Jedynska</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ElaineFuertes">Elaine Fuertes</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://umcutrecht.academia.edu/HenrietteSmit">Henriette Smit</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Negative effects of long-term exposure to particulate matter (PM) on lung function have been show...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Negative effects of long-term exposure to particulate matter (PM) on lung function have been shown repeatedly. Spatial differences in the composition and toxicity of PM may explain differences in observed effect sizes between studies. We conducted a multicenter study in 5 European birth cohorts-BAMSE (Sweden), GINIplus and LISAplus (Germany), MAAS (United Kingdom), and PIAMA (The Netherlands)-for which lung function measurements were available for study subjects at the age of 6 or 8 years. Individual annual average residential exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM smaller than 2.5 μm (PM2.5) and smaller than 10 μm (PM10) was estimated using land-use regression models. Associations between air pollution and lung function were analyzed by linear regression within cohorts, adjusting for potential confounders, and then combined by random effects meta-analysis. We observed small reductions in forced expiratory volume in the first second, forced vital capacity, and peak expiratory flow related to exposure to most elemental pollutants, with the most substantial negative associations found for nickel and sulfur. PM10 nickel and PM10 sulfur were associated with decreases in forced expiratory volume in the first second of 1.6% (95% confidence interval = 0.4% to 2.7%) and 2.3% (-0.1% to 4.6%) per increase in exposure of 2 and 200 ng/m, respectively. Associations remained after adjusting for PM mass. However, associations with these elements were not evident in all cohorts, and heterogeneity of associations with exposure to various components was larger than for exposure to PM mass. Although we detected small adverse effects on lung function associated with annual average levels of some of the evaluated elements (particularly nickel and sulfur), lower lung function was more consistently associated with increased PM mass.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13012358"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13012358"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13012358; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13012358]").text(description); $(".js-view-count[data-work-id=13012358]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13012358; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13012358']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13012358, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13012358]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13012358,"title":"Elemental composition of particulate matter and the association with lung function","translated_title":"","metadata":{"abstract":"Negative effects of long-term exposure to particulate matter (PM) on lung function have been shown repeatedly. 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class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12916818/Ambient_air_pollution_and_low_birthweight_a_European_cohort_study_ESCAPE_">Ambient air pollution and low birthweight: a European cohort study (ESCAPE)</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" rel="nofollow" href="https://independent.academia.edu/ReginaGra%C5%BEulevi%C4%8Dien%C4%97">Regina Gražulevičienė</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://erasmusmc.academia.edu/EdithvandenHooven">Edith van den Hooven</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MichaelWilhelm2">Michael Wilhelm</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JohannaLepeule">Johanna Lepeule</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://inserm.academia.edu/R%C3%A9mySlama">Rémy Slama</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://uniovi.academia.edu/AnaFern%C3%A1ndezSomoano">Ana Fernández-Somoano</a></span></div><div class="wp-workCard_item"><span>The Lancet. Respiratory medicine</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ambient air pollution has been associated with restricted fetal growth, which is linked with adve...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Ambient air pollution has been associated with restricted fetal growth, which is linked with adverse respiratory health in childhood. We assessed the effect of maternal exposure to low concentrations of ambient air pollution on birthweight. We pooled data from 14 population-based mother-child cohort studies in 12 European countries. Overall, the study population included 74 178 women who had singleton deliveries between Feb 11, 1994, and June 2, 2011, and for whom information about infant birthweight, gestational age, and sex was available. The primary outcome of interest was low birthweight at term (weight &lt;2500 g at birth after 37 weeks of gestation). Mean concentrations of particulate matter with an aerodynamic diameter of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 2·5 μm and 10 μm during pregnancy were estimated at maternal home addresses with temporally adjusted land-use regression models, as was PM2·5 absorbance and concentrations of nitrogen dioxide (NO2...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="06d9ea55231058a210a941fe39e00c24" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45847801,"asset_id":12916818,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45847801/download_file?st=MTczMzAxMTI3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12916818"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12916818"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12916818; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12916818]").text(description); $(".js-view-count[data-work-id=12916818]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12916818; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12916818']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12916818, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "06d9ea55231058a210a941fe39e00c24" } } $('.js-work-strip[data-work-id=12916818]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12916818,"title":"Ambient air pollution and low birthweight: a European cohort study (ESCAPE)","translated_title":"","metadata":{"abstract":"Ambient air pollution has been associated with restricted fetal growth, which is linked with adverse respiratory health in childhood. 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href="https://www.academia.edu/13012357/Associations_between_particulate_matter_elements_and_early_life_pneumonia_in_seven_birth_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects"><img alt="Research paper thumbnail of Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/13012357/Associations_between_particulate_matter_elements_and_early_life_pneumonia_in_seven_birth_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects">Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AleksandraJedynska">Aleksandra Jedynska</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://umcutrecht.academia.edu/HenrietteSmit">Henriette Smit</a></span></div><div class="wp-workCard_item"><span>International Journal of Hygiene and Environmental Health</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Evidence for a role of long-term particulate matter exposure on acute respiratory infections is g...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13012357"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13012357"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13012357; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13012357]").text(description); $(".js-view-count[data-work-id=13012357]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13012357; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13012357']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13012357, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13012357]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13012357,"title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects","translated_title":"","metadata":{"abstract":"Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"International Journal of Hygiene and Environmental Health"},"translated_abstract":"Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.","internal_url":"https://www.academia.edu/13012357/Associations_between_particulate_matter_elements_and_early_life_pneumonia_in_seven_birth_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects","translated_internal_url":"","created_at":"2015-06-15T23:58:31.533-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32237606,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":1021581,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32826726,"co_author_invite_id":284105,"email":"m***k@ki.se","affiliation":"Karolinska Institutet","display_order":0,"name":"Michal Korek","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021571,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021635,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32284352,"co_author_invite_id":349961,"email":"e***s@interchange.ubc.ca","display_order":null,"name":"Elaine Fuertes","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021541,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32728784,"co_author_invite_id":283632,"email":"g***i@deplazio.it","display_order":null,"name":"Giulia Cesaroni","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021537,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":135498,"email":"m***n@creal.cat","display_order":null,"name":"Mark Nieuwenhuijsen","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021624,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32697911,"co_author_invite_id":295817,"email":"u***g@uu.nl","display_order":null,"name":"Ulrike Gehring","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021551,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":284112,"email":"b***f@vet.uu.nl","display_order":null,"name":"Bert Brunekreef","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021560,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":284113,"email":"r***n@uu.nl","display_order":null,"name":"R. 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Matter","url":"https://www.academia.edu/Documents/in/Particulate_Matter"},{"id":974616,"name":"Jupiter","url":"https://www.academia.edu/Documents/in/Jupiter"},{"id":1294607,"name":"Logistic Models","url":"https://www.academia.edu/Documents/in/Logistic_Models"},{"id":1312021,"name":"Environmental Exposure","url":"https://www.academia.edu/Documents/in/Environmental_Exposure"},{"id":1656539,"name":"Air Pollutants","url":"https://www.academia.edu/Documents/in/Air_Pollutants"},{"id":1819400,"name":"Cohort Studies","url":"https://www.academia.edu/Documents/in/Cohort_Studies"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857209"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China"><img alt="Research paper thumbnail of Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China" class="work-thumbnail" src="https://attachments.academia-assets.com/45887150/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China">Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/NancyPedersen">Nancy Pedersen</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span>PLoS ONE</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28e01eea93bf28cc493f57993fb477e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "28e01eea93bf28cc493f57993fb477e6" } } $('.js-work-strip[data-work-id=12857209]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857209,"title":"Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China","translated_title":"","metadata":{"grobid_abstract":"Air pollutants (AP) play a role in subclinical inflammation, and are associated with cardiovascular morbidity and mortality. Metabolic syndrome (MetS) is inflammatory and precedes cardiovascular morbidity and type 2 diabetes. Thus, a positive association between AP and MetS may be hypothesized. We explored this association, (taking into account, pathwayspecific MetS definitions), and its potential modifiers in Swiss adults. We studied 3769 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults, reporting at least four-hour fasting time before venepuncture. AP exposures were 10-year mean residential PM 10 (particulate matter \u003c10μm in diameter) and NO 2 (nitrogen dioxide). Outcomes included MetS defined by World Health Organization (MetS-W), International Diabetes Federation (MetS-I) and Adult Treatment Panel-III (MetS-A) using four-and eighthour fasting time limits. We also explored associations with individual components of MetS. We applied mixed logistic regression models to explore these associations. The prevalence of MetS-W, MetS-I and MetS-A were 10%, 22% and 18% respectively. Odds of MetS-W, MetS-I and MetS-A increased by 72% (51-102%), 31% (11-54%) and 18% (4-34%) per 10μg/m 3 increase in 10-year mean PM 10 . We observed weaker associations with NO 2 . Associations were stronger among physically-active, ever-smokers and non-diabetic participants especially with PM 10 (p\u003c0.05). Associations remained robust across various sensitivity analyses including ten imputations of missing observations and exclusion of diabetes cases. The observed associations between AP exposure and MetS were sensitive to MetS definitions. Regarding the MetS components, we observed strongest associations with impaired fasting glycemia, and positive but weaker associations with hypertension and waist-circumference-based obesity. Cardio-metabolic effects of AP may be majorly driven by impairment of glucose homeostasis, and to a less-strong extent, visceral adiposity. 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src="https://attachments.academia-assets.com/45898827/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12840189/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project">Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JoachimHeinrich">Joachim Heinrich</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AntoniaTrichopoulou">Antonia Trichopoulou</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/FulvioRicceri">Fulvio Ricceri</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ClaudiaGalassi">Claudia Galassi</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>The Lancet</span><span>, 2014</span></div><div 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wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/13683429/Meta_analysis_of_air_pollution_exposure_association_with_allergic_sensitization_in_European_birth_cohorts">Meta-analysis of air pollution exposure association with allergic sensitization in European birth cohorts</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnaM%C3%B6lter">Anna Mölter</a></span></div><div class="wp-workCard_item"><span>Journal of Allergy and Clinical Immunology</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a 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href="https://www.academia.edu/13683427/Long_term_Exposure_to_Air_Pollution_and_Cardiovascular_Mortality">Long-term Exposure to Air Pollution and Cardiovascular Mortality</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/IbonTamayo">Ibon Tamayo</a></span></div><div class="wp-workCard_item"><span>Epidemiology</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6c1d47d74c2dc0df46638a0e78ae602" 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href="https://www.academia.edu/12840177/Development_of_Land_Use_Regression_Models_for_Particle_Composition_in_Twenty_Study_Areas_in_Europe"><img alt="Research paper thumbnail of Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12840177/Development_of_Land_Use_Regression_Models_for_Particle_Composition_in_Twenty_Study_Areas_in_Europe">Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnaM%C3%B6lter">Anna Mölter</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>Environmental Science & Technology</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Land Use Regression (LUR) models have been used to describe and model spatial variability of annu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12840177"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12840177"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12840177; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12840177]").text(description); $(".js-view-count[data-work-id=12840177]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12840177; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12840177']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12840177, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12840177]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12840177,"title":"Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe","translated_title":"","metadata":{"abstract":"Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). 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href="https://www.academia.edu/12916812/Air_Pollution_and_Respiratory_Infections_during_Early_Childhood_An_Analysis_of_10_European_Birth_Cohorts_within_the_ESCAPE_Project"><img alt="Research paper thumbnail of Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project" class="work-thumbnail" src="https://attachments.academia-assets.com/45847754/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12916812/Air_Pollution_and_Respiratory_Infections_during_Early_Childhood_An_Analysis_of_10_European_Birth_Cohorts_within_the_ESCAPE_Project">Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ChiaraBadaloni">Chiara Badaloni</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://uniovi.academia.edu/AnaFern%C3%A1ndezSomoano">Ana Fernández-Somoano</a></span></div><div class="wp-workCard_item"><span>Environmental Health Perspectives</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c5ea9eab8ef68be6670fe9c9fb3aed7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45847754,"asset_id":12916812,"asset_type":"Work","button_location":"profile"}" 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cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/12840176/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects">Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/FulvioRicceri">Fulvio Ricceri</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a></span></div><div class="wp-workCard_item"><span>Environment International</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span 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in Europe – The ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://uhasselt.academia.edu/EviDons">Evi Dons</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/GiuliaCesaroni">Giulia Cesaroni</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ChiaraBadaloni">Chiara Badaloni</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>Atmospheric Environment</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a 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src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13683434/Traffic_related_air_pollution_exposure_and_incidence_of_stroke_in_four_cohorts_from_Stockholm">Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm</a></div><div class="wp-workCard_item"><span>Journal of exposure science & environmental epidemiology</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigated the risk of stroke related to long-term ambient air pollution exposure, in partic...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigated the risk of stroke related to long-term ambient air pollution exposure, in particular the role of various exposure time windows, using four cohorts from Stockholm County, Sweden. In total, 22,587 individuals were recruited from 1992 to 2004 and followed until 2011. Yearly air pollution levels resulting from local road traffic emissions were assessed at participant residences using dispersion models for particulate matter (PM10) and nitrogen oxides (NOX). Cohort-specific hazard ratios were estimated for time-weighted air pollution exposure during different time windows and the incidence of stroke, adjusted for common risk factors, and then meta-analysed. Overall, 868 subjects suffered a non-fatal or fatal stroke during 238,731 person-years of follow-up. An increment of 20 μg/m(3) in estimated annual mean of road-traffic related NOX exposure at recruitment was associated with a hazard ratio of 1.16 (95% CI 0.83-1.61), with evidence of heterogeneity between the cohorts....</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13683434"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13683434"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13683434; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13683434]").text(description); $(".js-view-count[data-work-id=13683434]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13683434; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13683434']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13683434, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13683434]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13683434,"title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm","translated_title":"","metadata":{"abstract":"We investigated the risk of stroke related to long-term ambient air pollution exposure, in particular the role of various exposure time windows, using four cohorts from Stockholm County, Sweden. In total, 22,587 individuals were recruited from 1992 to 2004 and followed until 2011. Yearly air pollution levels resulting from local road traffic emissions were assessed at participant residences using dispersion models for particulate matter (PM10) and nitrogen oxides (NOX). Cohort-specific hazard ratios were estimated for time-weighted air pollution exposure during different time windows and the incidence of stroke, adjusted for common risk factors, and then meta-analysed. Overall, 868 subjects suffered a non-fatal or fatal stroke during 238,731 person-years of follow-up. An increment of 20 μg/m(3) in estimated annual mean of road-traffic related NOX exposure at recruitment was associated with a hazard ratio of 1.16 (95% CI 0.83-1.61), with evidence of heterogeneity between the cohorts....","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Journal of exposure science \u0026 environmental epidemiology"},"translated_abstract":"We investigated the risk of stroke related to long-term ambient air pollution exposure, in particular the role of various exposure time windows, using four cohorts from Stockholm County, Sweden. In total, 22,587 individuals were recruited from 1992 to 2004 and followed until 2011. Yearly air pollution levels resulting from local road traffic emissions were assessed at participant residences using dispersion models for particulate matter (PM10) and nitrogen oxides (NOX). Cohort-specific hazard ratios were estimated for time-weighted air pollution exposure during different time windows and the incidence of stroke, adjusted for common risk factors, and then meta-analysed. Overall, 868 subjects suffered a non-fatal or fatal stroke during 238,731 person-years of follow-up. An increment of 20 μg/m(3) in estimated annual mean of road-traffic related NOX exposure at recruitment was associated with a hazard ratio of 1.16 (95% CI 0.83-1.61), with evidence of heterogeneity between the cohorts....","internal_url":"https://www.academia.edu/13683434/Traffic_related_air_pollution_exposure_and_incidence_of_stroke_in_four_cohorts_from_Stockholm","translated_internal_url":"","created_at":"2015-07-06T00:24:42.116-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32826726,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":2378660,"work_id":13683434,"tagging_user_id":32826726,"tagged_user_id":31999659,"co_author_invite_id":null,"email":"n***n@ki.se","display_order":0,"name":"Nancy Pedersen","title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm"},{"id":2378735,"work_id":13683434,"tagging_user_id":32826726,"tagged_user_id":35007401,"co_author_invite_id":284054,"email":"k***r@ki.se","display_order":4194304,"name":"Karin Leander","title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm"},{"id":2378755,"work_id":13683434,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":295819,"email":"t***r@ki.se","display_order":6291456,"name":"Tom Bellander","title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm"},{"id":2378766,"work_id":13683434,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":272046,"email":"p***n@ki.se","display_order":7340032,"name":"Patrik Magnusson","title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm"},{"id":2378768,"work_id":13683434,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":647839,"email":"m***i@ki.se","display_order":7864320,"name":"Matteo Bottai","title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm"},{"id":2378774,"work_id":13683434,"tagging_user_id":32826726,"tagged_user_id":null,"co_author_invite_id":284109,"email":"g***n@ki.se","display_order":8126464,"name":"Göran Pershagen","title":"Traffic-related air pollution exposure and incidence of stroke in four cohorts from Stockholm"}],"downloadable_attachments":[],"slug":"Traffic_related_air_pollution_exposure_and_incidence_of_stroke_in_four_cohorts_from_Stockholm","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32826726,"first_name":"Michal","middle_initials":null,"last_name":"Korek","page_name":"MichalKorek","domain_name":"ki","created_at":"2015-07-06T00:23:47.506-07:00","display_name":"Michal Korek","url":"https://ki.academia.edu/MichalKorek"},"attachments":[],"research_interests":[{"id":1085,"name":"Epidemiology","url":"https://www.academia.edu/Documents/in/Epidemiology"},{"id":16664,"name":"Risk assessment","url":"https://www.academia.edu/Documents/in/Risk_assessment"},{"id":53529,"name":"Exposure Assessment","url":"https://www.academia.edu/Documents/in/Exposure_Assessment"},{"id":58054,"name":"Environmental Sciences","url":"https://www.academia.edu/Documents/in/Environmental_Sciences"},{"id":222437,"name":"Exposure","url":"https://www.academia.edu/Documents/in/Exposure"},{"id":260118,"name":"CHEMICAL SCIENCES","url":"https://www.academia.edu/Documents/in/CHEMICAL_SCIENCES"},{"id":622589,"name":"Risk Assessment","url":"https://www.academia.edu/Documents/in/Risk_Assessment-2"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="13683433"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/13683433/Arterial_Blood_Pressure_and_Long_Term_Exposure_to_Traffic_Related_Air_Pollution_An_Analysis_in_the_European_Study_of_Cohorts_for_Air_Pollution_Effects_ESCAPE_"><img alt="Research paper thumbnail of Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13683433/Arterial_Blood_Pressure_and_Long_Term_Exposure_to_Traffic_Related_Air_Pollution_An_Analysis_in_the_European_Study_of_Cohorts_for_Air_Pollution_Effects_ESCAPE_">Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)</a></div><div class="wp-workCard_item"><span>Environmental Health Perspectives</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP)...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific. We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations. We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis. In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated. In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. The relationship of modeled air pollutants with BP was inconsistent.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13683433"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13683433"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13683433; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13683433]").text(description); $(".js-view-count[data-work-id=13683433]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13683433; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13683433']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13683433, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13683433]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13683433,"title":"Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)","translated_title":"","metadata":{"abstract":"Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific. We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations. We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis. In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated. In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. The relationship of modeled air pollutants with BP was inconsistent.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Environmental Health Perspectives"},"translated_abstract":"Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific. We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations. We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis. In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated. In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12890199"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12890199/Comparing_land_use_regression_and_dispersion_modelling_to_assess_residential_exposure_to_ambient_air_pollution_for_epidemiological_studies"><img alt="Research paper thumbnail of Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies" class="work-thumbnail" src="https://attachments.academia-assets.com/60076889/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12890199/Comparing_land_use_regression_and_dispersion_modelling_to_assess_residential_exposure_to_ambient_air_pollution_for_epidemiological_studies">Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ChiaraBadaloni">Chiara Badaloni</a></span></div><div class="wp-workCard_item"><span>Environment International</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec25da2d01f0ea27717e99b5095b2830" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60076889,"asset_id":12890199,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60076889/download_file?st=MTczMzAxMTI3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12890199"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12890199"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12890199; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12890199]").text(description); $(".js-view-count[data-work-id=12890199]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12890199; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12890199']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12890199, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ec25da2d01f0ea27717e99b5095b2830" } } $('.js-work-strip[data-work-id=12890199]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12890199,"title":"Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies","translated_title":"","metadata":{"abstract":"Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. 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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/13683432/Performance_of_multi_city_land_use_regression_models_for_nitrogen_dioxide_and_fine_particles"><img alt="Research paper thumbnail of Performance of multi-city land use regression models for nitrogen dioxide and fine particles" class="work-thumbnail" src="https://attachments.academia-assets.com/45063866/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/13683432/Performance_of_multi_city_land_use_regression_models_for_nitrogen_dioxide_and_fine_particles">Performance of multi-city land use regression models for nitrogen dioxide and fine particles</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="13683431"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/13683431/A_multicentre_study_of_air_pollution_exposure_and_childhood_asthma_prevalence_the_ESCAPE_project"><img alt="Research paper thumbnail of A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13683431/A_multicentre_study_of_air_pollution_exposure_and_childhood_asthma_prevalence_the_ESCAPE_project">A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnaM%C3%B6lter">Anna Mölter</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The aim of this study was to determine the effect of six traffic-related air pollution metrics (n...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;10 μm (PM10), PM2.5, coarse particulate matter and PM2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, divided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4-5 and 8-10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from individual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure (e.g. adjusted OR (95%CI) for asthma at age 8-10 years and exposure at the birth address (n=10377): 1.10 (0.81-1.49) per 10 μg·m(-3) nitrogen dioxide; 0.88 (0.63-1.24) per 10 μg·m(-3) PM10; 1.23 (0.78-1.95) per 5 μg·m(-3) PM2.5). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. 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with lung function" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13012358/Elemental_composition_of_particulate_matter_and_the_association_with_lung_function">Elemental composition of particulate matter and the association with lung function</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AleksandraJedynska">Aleksandra Jedynska</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ElaineFuertes">Elaine Fuertes</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://umcutrecht.academia.edu/HenrietteSmit">Henriette Smit</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Negative effects of long-term exposure to particulate matter (PM) on lung function have been show...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Negative effects of long-term exposure to particulate matter (PM) on lung function have been shown repeatedly. Spatial differences in the composition and toxicity of PM may explain differences in observed effect sizes between studies. We conducted a multicenter study in 5 European birth cohorts-BAMSE (Sweden), GINIplus and LISAplus (Germany), MAAS (United Kingdom), and PIAMA (The Netherlands)-for which lung function measurements were available for study subjects at the age of 6 or 8 years. Individual annual average residential exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM smaller than 2.5 μm (PM2.5) and smaller than 10 μm (PM10) was estimated using land-use regression models. Associations between air pollution and lung function were analyzed by linear regression within cohorts, adjusting for potential confounders, and then combined by random effects meta-analysis. We observed small reductions in forced expiratory volume in the first second, forced vital capacity, and peak expiratory flow related to exposure to most elemental pollutants, with the most substantial negative associations found for nickel and sulfur. PM10 nickel and PM10 sulfur were associated with decreases in forced expiratory volume in the first second of 1.6% (95% confidence interval = 0.4% to 2.7%) and 2.3% (-0.1% to 4.6%) per increase in exposure of 2 and 200 ng/m, respectively. Associations remained after adjusting for PM mass. However, associations with these elements were not evident in all cohorts, and heterogeneity of associations with exposure to various components was larger than for exposure to PM mass. Although we detected small adverse effects on lung function associated with annual average levels of some of the evaluated elements (particularly nickel and sulfur), lower lung function was more consistently associated with increased PM mass.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13012358"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13012358"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13012358; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13012358]").text(description); $(".js-view-count[data-work-id=13012358]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13012358; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13012358']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13012358, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13012358]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13012358,"title":"Elemental composition of particulate matter and the association with lung function","translated_title":"","metadata":{"abstract":"Negative effects of long-term exposure to particulate matter (PM) on lung function have been shown repeatedly. 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class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12916818/Ambient_air_pollution_and_low_birthweight_a_European_cohort_study_ESCAPE_">Ambient air pollution and low birthweight: a European cohort study (ESCAPE)</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" rel="nofollow" href="https://independent.academia.edu/ReginaGra%C5%BEulevi%C4%8Dien%C4%97">Regina Gražulevičienė</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://erasmusmc.academia.edu/EdithvandenHooven">Edith van den Hooven</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MichaelWilhelm2">Michael Wilhelm</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JohannaLepeule">Johanna Lepeule</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://inserm.academia.edu/R%C3%A9mySlama">Rémy Slama</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://uniovi.academia.edu/AnaFern%C3%A1ndezSomoano">Ana Fernández-Somoano</a></span></div><div class="wp-workCard_item"><span>The Lancet. Respiratory medicine</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ambient air pollution has been associated with restricted fetal growth, which is linked with adve...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Ambient air pollution has been associated with restricted fetal growth, which is linked with adverse respiratory health in childhood. We assessed the effect of maternal exposure to low concentrations of ambient air pollution on birthweight. We pooled data from 14 population-based mother-child cohort studies in 12 European countries. Overall, the study population included 74 178 women who had singleton deliveries between Feb 11, 1994, and June 2, 2011, and for whom information about infant birthweight, gestational age, and sex was available. The primary outcome of interest was low birthweight at term (weight &lt;2500 g at birth after 37 weeks of gestation). Mean concentrations of particulate matter with an aerodynamic diameter of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 2·5 μm and 10 μm during pregnancy were estimated at maternal home addresses with temporally adjusted land-use regression models, as was PM2·5 absorbance and concentrations of nitrogen dioxide (NO2...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="06d9ea55231058a210a941fe39e00c24" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45847801,"asset_id":12916818,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45847801/download_file?st=MTczMzAxMTI3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12916818"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12916818"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12916818; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12916818]").text(description); $(".js-view-count[data-work-id=12916818]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12916818; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12916818']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12916818, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "06d9ea55231058a210a941fe39e00c24" } } $('.js-work-strip[data-work-id=12916818]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12916818,"title":"Ambient air pollution and low birthweight: a European cohort study (ESCAPE)","translated_title":"","metadata":{"abstract":"Ambient air pollution has been associated with restricted fetal growth, which is linked with adverse respiratory health in childhood. 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href="https://www.academia.edu/13012357/Associations_between_particulate_matter_elements_and_early_life_pneumonia_in_seven_birth_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects"><img alt="Research paper thumbnail of Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/13012357/Associations_between_particulate_matter_elements_and_early_life_pneumonia_in_seven_birth_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects">Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AleksandraJedynska">Aleksandra Jedynska</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://umcutrecht.academia.edu/HenrietteSmit">Henriette Smit</a></span></div><div class="wp-workCard_item"><span>International Journal of Hygiene and Environmental Health</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Evidence for a role of long-term particulate matter exposure on acute respiratory infections is g...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13012357"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13012357"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13012357; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13012357]").text(description); $(".js-view-count[data-work-id=13012357]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13012357; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13012357']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 13012357, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13012357]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13012357,"title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects","translated_title":"","metadata":{"abstract":"Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"International Journal of Hygiene and Environmental Health"},"translated_abstract":"Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.","internal_url":"https://www.academia.edu/13012357/Associations_between_particulate_matter_elements_and_early_life_pneumonia_in_seven_birth_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects","translated_internal_url":"","created_at":"2015-06-15T23:58:31.533-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32237606,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":1021581,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32826726,"co_author_invite_id":284105,"email":"m***k@ki.se","affiliation":"Karolinska Institutet","display_order":0,"name":"Michal Korek","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021571,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021635,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32284352,"co_author_invite_id":349961,"email":"e***s@interchange.ubc.ca","display_order":null,"name":"Elaine Fuertes","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021541,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32728784,"co_author_invite_id":283632,"email":"g***i@deplazio.it","display_order":null,"name":"Giulia Cesaroni","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021537,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":135498,"email":"m***n@creal.cat","display_order":null,"name":"Mark Nieuwenhuijsen","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021624,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":32697911,"co_author_invite_id":295817,"email":"u***g@uu.nl","display_order":null,"name":"Ulrike Gehring","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021551,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":284112,"email":"b***f@vet.uu.nl","display_order":null,"name":"Bert Brunekreef","title":"Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":1021560,"work_id":13012357,"tagging_user_id":32237606,"tagged_user_id":null,"co_author_invite_id":284113,"email":"r***n@uu.nl","display_order":null,"name":"R. 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Matter","url":"https://www.academia.edu/Documents/in/Particulate_Matter"},{"id":974616,"name":"Jupiter","url":"https://www.academia.edu/Documents/in/Jupiter"},{"id":1294607,"name":"Logistic Models","url":"https://www.academia.edu/Documents/in/Logistic_Models"},{"id":1312021,"name":"Environmental Exposure","url":"https://www.academia.edu/Documents/in/Environmental_Exposure"},{"id":1656539,"name":"Air Pollutants","url":"https://www.academia.edu/Documents/in/Air_Pollutants"},{"id":1819400,"name":"Cohort Studies","url":"https://www.academia.edu/Documents/in/Cohort_Studies"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857209"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China"><img alt="Research paper thumbnail of Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China" class="work-thumbnail" src="https://attachments.academia-assets.com/45887150/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China">Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/NancyPedersen">Nancy Pedersen</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span>PLoS ONE</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28e01eea93bf28cc493f57993fb477e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "28e01eea93bf28cc493f57993fb477e6" } } $('.js-work-strip[data-work-id=12857209]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857209,"title":"Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China","translated_title":"","metadata":{"grobid_abstract":"Air pollutants (AP) play a role in subclinical inflammation, and are associated with cardiovascular morbidity and mortality. Metabolic syndrome (MetS) is inflammatory and precedes cardiovascular morbidity and type 2 diabetes. Thus, a positive association between AP and MetS may be hypothesized. We explored this association, (taking into account, pathwayspecific MetS definitions), and its potential modifiers in Swiss adults. We studied 3769 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults, reporting at least four-hour fasting time before venepuncture. AP exposures were 10-year mean residential PM 10 (particulate matter \u003c10μm in diameter) and NO 2 (nitrogen dioxide). Outcomes included MetS defined by World Health Organization (MetS-W), International Diabetes Federation (MetS-I) and Adult Treatment Panel-III (MetS-A) using four-and eighthour fasting time limits. We also explored associations with individual components of MetS. We applied mixed logistic regression models to explore these associations. The prevalence of MetS-W, MetS-I and MetS-A were 10%, 22% and 18% respectively. Odds of MetS-W, MetS-I and MetS-A increased by 72% (51-102%), 31% (11-54%) and 18% (4-34%) per 10μg/m 3 increase in 10-year mean PM 10 . We observed weaker associations with NO 2 . Associations were stronger among physically-active, ever-smokers and non-diabetic participants especially with PM 10 (p\u003c0.05). Associations remained robust across various sensitivity analyses including ten imputations of missing observations and exclusion of diabetes cases. The observed associations between AP exposure and MetS were sensitive to MetS definitions. Regarding the MetS components, we observed strongest associations with impaired fasting glycemia, and positive but weaker associations with hypertension and waist-circumference-based obesity. Cardio-metabolic effects of AP may be majorly driven by impairment of glucose homeostasis, and to a less-strong extent, visceral adiposity. 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src="https://attachments.academia-assets.com/45898827/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12840189/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project">Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JoachimHeinrich">Joachim Heinrich</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AntoniaTrichopoulou">Antonia Trichopoulou</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/FulvioRicceri">Fulvio Ricceri</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ClaudiaGalassi">Claudia Galassi</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>The Lancet</span><span>, 2014</span></div><div 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wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/13683429/Meta_analysis_of_air_pollution_exposure_association_with_allergic_sensitization_in_European_birth_cohorts">Meta-analysis of air pollution exposure association with allergic sensitization in European birth cohorts</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnaM%C3%B6lter">Anna Mölter</a></span></div><div class="wp-workCard_item"><span>Journal of Allergy and Clinical Immunology</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a 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href="https://www.academia.edu/13683427/Long_term_Exposure_to_Air_Pollution_and_Cardiovascular_Mortality">Long-term Exposure to Air Pollution and Cardiovascular Mortality</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/IbonTamayo">Ibon Tamayo</a></span></div><div class="wp-workCard_item"><span>Epidemiology</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6c1d47d74c2dc0df46638a0e78ae602" 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href="https://www.academia.edu/12840177/Development_of_Land_Use_Regression_Models_for_Particle_Composition_in_Twenty_Study_Areas_in_Europe"><img alt="Research paper thumbnail of Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12840177/Development_of_Land_Use_Regression_Models_for_Particle_Composition_in_Twenty_Study_Areas_in_Europe">Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnaM%C3%B6lter">Anna Mölter</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>Environmental Science & Technology</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Land Use Regression (LUR) models have been used to describe and model spatial variability of annu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12840177"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12840177"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12840177; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12840177]").text(description); $(".js-view-count[data-work-id=12840177]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12840177; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12840177']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12840177, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12840177]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12840177,"title":"Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe","translated_title":"","metadata":{"abstract":"Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). 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href="https://www.academia.edu/12916812/Air_Pollution_and_Respiratory_Infections_during_Early_Childhood_An_Analysis_of_10_European_Birth_Cohorts_within_the_ESCAPE_Project"><img alt="Research paper thumbnail of Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project" class="work-thumbnail" src="https://attachments.academia-assets.com/45847754/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12916812/Air_Pollution_and_Respiratory_Infections_during_Early_Childhood_An_Analysis_of_10_European_Birth_Cohorts_within_the_ESCAPE_Project">Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ChiaraBadaloni">Chiara Badaloni</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://uniovi.academia.edu/AnaFern%C3%A1ndezSomoano">Ana Fernández-Somoano</a></span></div><div class="wp-workCard_item"><span>Environmental Health Perspectives</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c5ea9eab8ef68be6670fe9c9fb3aed7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45847754,"asset_id":12916812,"asset_type":"Work","button_location":"profile"}" 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cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/12840176/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects">Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://swisstph.academia.edu/MingYiTsai">Ming-Yi Tsai</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/FulvioRicceri">Fulvio Ricceri</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a></span></div><div class="wp-workCard_item"><span>Environment International</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span 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in Europe – The ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://uhasselt.academia.edu/EviDons">Evi Dons</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/GiuliaCesaroni">Giulia Cesaroni</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ChiaraBadaloni">Chiara Badaloni</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>Atmospheric Environment</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a 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