{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T07:48:40Z","timestamp":1789372120154,"version":"build-2803163510"},"reference-count":31,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2009,9,1]],"date-time":"2009-09-01T00:00:00Z","timestamp":1251763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Environmetrics"],"published-print":{"date-parts":[[2010,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    We describe a methodology for assigning individual estimates of long\u2010term average air pollution concentrations that accounts for a complex spatio\u2010temporal correlation structure and can accommodate spatio\u2010temporally misaligned observations. This methodology has been developed as part of the Multi\u2010Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space\u2010\u2010time field into a \u201cmean\u201d that includes dependence on covariates and spatially varying seasonal and long\u2010term trends and a \u201cresidual\u201d that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio\u2010temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single\u2010stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long\u2010term average concentrations of oxides of nitrogen (NO\n                    <jats:sub>x<\/jats:sub>\n                    ) from 2005 to 2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross\u2010validated\n                    <jats:italic>\n                      IR\n                      <jats:sup>2<\/jats:sup>\n                    <\/jats:italic>\n                    is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long\u2010term average concentrations. Copyright \u00a9 2009 John Wiley &amp; Sons, Ltd.\n                  <\/jats:p>","DOI":"10.1002\/env.1014","type":"journal-article","created":{"date-parts":[[2009,9,1]],"date-time":"2009-09-01T23:29:48Z","timestamp":1251847788000},"page":"606-631","source":"Crossref","is-referenced-by-count":127,"title":["Predicting intra\u2010urban variation in air pollution concentrations with complex spatio\u2010temporal dependencies"],"prefix":"10.1002","volume":"21","author":[{"given":"Adam A.","family":"Szpiro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul D.","family":"Sampson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianne","family":"Sheppard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Lumley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara D.","family":"Adar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel D.","family":"Kaufman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2009,9]]},"reference":[{"key":"e_1_2_1_2_1","volume-title":"Hierarchical Modeling and Analysis for Spatial Data","author":"Banerjee S","year":"2004"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM200012143432401"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.EDE.0000041910.49046.9B"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1137\/0916069"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1021\/es8030837"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119115151"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1029\/2002JD002864"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199312093292401"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg178"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/env.889"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420011050.ch3"},{"key":"e_1_2_1_13_1","unstructured":"GotoK.2008. 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