{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:55:39Z","timestamp":1788188139921,"version":"build-2803163510"},"reference-count":57,"publisher":"American Chemical Society (ACS)","issue":"11","license":[{"start":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T00:00:00Z","timestamp":1514505600000},"content-version":"vor","delay-in-days":3105,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Epidemiologic studies of air pollution have demonstrated a link between long-term air pollution exposures and mortality. However, many have been limited to city-specific average pollution measures or spatial or land-use regression exposure models in small geographic areas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Our objective was to develop nationwide models of annual exposure to particulate matter &amp;lt; 10 \u03bcm in diameter (PM10) and nitrogen dioxide during 1985\u20132000.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used generalized additive models (GAMs) to predict annual levels of the pollutants using smooth spatial surfaces of available monitoring data and geographic information system\u2013derived covariates. Model performance was determined using a cross-validation (CV) procedure with 10% of the data. We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For PM10, distance to road, elevation, proportion of low-intensity residential, high-intensity residential, and industrial, commercial, or transportation land use within 1 km were all statistically significant predictors of measured PM10 (model R2 = 0.49, CV R2 = 0.55). Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides\u2013emitting power plant were all statistically significant predictors of measured NO2 (model R2 = 0.88, CV R2 = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R2 and higher precision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These models provide reasonably accurate and unbiased estimates of annual exposures for PM10 and NO2. This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1289\/ehp.0900840","type":"journal-article","created":{"date-parts":[[2009,6,29]],"date-time":"2009-06-29T13:52:11Z","timestamp":1246283531000},"page":"1690-1696","source":"Crossref","is-referenced-by-count":72,"title":["Spatial Modeling of PM10 and NO2 in the Continental United States, 1985\u20132000"],"prefix":"10.1021","volume":"117","author":[{"given":"Jaime E.","family":"Hart","sequence":"first","affiliation":[{"name":"Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA"},{"name":"Channing Laboratory, Department of Medicine, Brigham and Women\u2019s Hospital and Harvard Medical School, Boston, Massachusetts, USA"},{"name":"Department of Epidemiology, Harvard School of Public Health, Harvard 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