{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T06:53:30Z","timestamp":1791269610389,"version":"4.1.0"},"reference-count":37,"publisher":"American Chemical Society (ACS)","issue":"6","license":[{"start":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T00:00:00Z","timestamp":1514505600000},"content-version":"vor","delay-in-days":2137,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Background: Epidemiologic studies of fine particulate matter [aerodynamic diameter \u2264 2.5 \u03bcm (PM2.5)] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (Finf) will affect epidemiologic study results.<\/jats:p>\n                  <jats:p>Objective: We aimed to develop models to predict Finf for &amp;gt; 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM2.5 exposure, subclinical cardiovascular disease, and clinical outcomes.<\/jats:p>\n                  <jats:p>Methods: We collected 526 two-week, paired indoor\u2013outdoor PM2.5 filter samples from a subset of study homes. PM2.5 elemental composition was measured by X-ray fluorescence, and Finf was estimated as the indoor\/outdoor sulfur ratio. We regressed Finf on meteorologic variables and questionnaire-based predictors in season-specific models. Models were evaluated using the R2 and root mean square error (RMSE) from a 10-fold cross-validation.<\/jats:p>\n                  <jats:p>Results: The mean \u00b1 SD Finf across all communities and seasons was 0.62 \u00b1 0.21, and community-specific means ranged from 0.47 \u00b1 0.15 in Winston-Salem, North Carolina, to 0.82 \u00b1 0.14 in New York, New York. Finf was generally greater during the warm (&amp;gt; 18\u00b0C) season. Central air conditioning (AC) use, frequency of AC use, and window opening frequency were the most important predictors during the warm season; outdoor temperature and forced-air heat were the best cold-season predictors. The models predicted 60% of the variance in 2-week Finf, with an RMSE of 0.13.<\/jats:p>\n                  <jats:p>Conclusions: We developed intuitive models that can predict Finf using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential Finf into an exposure assessment.<\/jats:p>","DOI":"10.1289\/ehp.1104447","type":"journal-article","created":{"date-parts":[[2012,2,22]],"date-time":"2012-02-22T15:12:48Z","timestamp":1329923568000},"page":"824-830","source":"Crossref","is-referenced-by-count":155,"title":["Modeling the Residential Infiltration of Outdoor PM2.5 in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)"],"prefix":"10.1021","volume":"120","author":[{"given":"Ryan W.","family":"Allen","sequence":"first","affiliation":[{"name":"Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara D.","family":"Adar","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ed","family":"Avol","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Cohen","sequence":"additional","affiliation":[{"name":"Department of Environmental and Occupational Health Sciences, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cynthia L.","family":"Curl","sequence":"additional","affiliation":[{"name":"Department of Environmental and Occupational Health Sciences, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Larson","sequence":"additional","affiliation":[{"name":"Department of Environmental and Occupational Health Sciences, and"},{"name":"Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L.-J. Sally","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Environmental and Occupational Health Sciences, and"},{"name":"Institute of Social and Preventive Medicine, University of Basel, Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianne","family":"Sheppard","sequence":"additional","affiliation":[{"name":"Department of Environmental and Occupational Health Sciences, and"},{"name":"Department of Biostatistics,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel D.","family":"Kaufman","sequence":"additional","affiliation":[{"name":"Department of Environmental and Occupational Health Sciences, and"},{"name":"Department of Epidemiology, and"},{"name":"Department of Medicine, University of Washington, Seattle, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2012,2,22]]},"reference":[{"issue":"4","key":"2026080507030489200_micit1","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/08958370801903826","article-title":"Changes in lung function and airway inflammation among asthmatic children residing in a woodsmoke-impacted urban area.","volume":"20","author":"Allen","year":"2008","journal-title":"Inhal Toxicol"},{"issue":"8","key":"2026080507030489200_micit2","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1080\/10473289.2006.10464529","article-title":"Characterization of major chemical components of fine particulate matter in North Carolina.","volume":"56","author":"Aneja","year":"2006","journal-title":"J Air Waste Manag Assoc"},{"issue":"5","key":"2026080507030489200_micit3","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1097\/EDE.0b013e3181aba749","article-title":"Adverse health effects of particulate air pollution modification by air conditioning.","volume":"20","author":"Bell","year":"2009","journal-title":"Epidemiology"},{"issue":"9","key":"2026080507030489200_micit4","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1093\/aje\/kwf113","article-title":"Multi-Ethnic Study of Atherosclerosis: objectives and design.","volume":"156","author":"Bild","year":"2002","journal-title":"Am J Epidemiol"},{"issue":"2","key":"2026080507030489200_micit5","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.atmosenv.2010.09.048","article-title":"Review of relationship between indoor and outdoor particles: I\/O ratio, infiltration factor and penetration factor.","volume":"45","author":"Chen","year":"2011","journal-title":"Atmos Environ"},{"issue":"8","key":"2026080507030489200_micit6","doi-asserted-by":"crossref","first-page":"3211","DOI":"10.3390\/ijerph7083211","article-title":"Exploring variation and predictors of residential fine particulate matter infiltration.","volume":"7","author":"Clark","year":"2010","journal-title":"Int J Environ Res Public Health"},{"issue":"13","key":"2026080507030489200_micit7","doi-asserted-by":"crossref","first-page":"4687","DOI":"10.1021\/es8030837","article-title":"Approach to estimating participant pollutant exposures in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).","volume":"43","author":"Cohen","year":"2009","journal-title":"Environ Sci Technol"},{"issue":"3","key":"2026080507030489200_micit8","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1097\/01.ede.0000158918.57071.3e","article-title":"Exposure to ambient and nonambient components of particulate matter\u2014a comparison of health effects.","volume":"16","author":"Ebelt","year":"2005","journal-title":"Epidemiology"},{"key":"2026080507030489200_micit9","article-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction.","author":"Hastie","year":"2001"},{"key":"2026080507030489200_micit10","unstructured":"Health Effects Institute\n          . 2010. 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