Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring

Environmental Health Perspectives

Volumn 125, Issue 1, 2017, Pages 38-46

  Kim, Sun Young ^a,b   Olives, Casey ^b   Sheppard, Lianne ^b   Sampson, Paul D ^b   Larson, Timothy V ^b,c   Keller, Joshua P ^b   Kaufman, Joel D ^b  

^a Seoul National University   (South Korea)

^b UNIVERSITY OF WASHINGTON   (United States)

^c University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COHORT ANALYSIS; CONCENTRATION (PARAMETERS); DEMOGRAPHY; ENVIRONMENTAL PARAMETERS; GEOCODING; HISTORICAL PREDICTION MODELING; MATHEMATICAL PARAMETERS; MODEL; MONITORING; PARTICULATE MATTER; PREDICTION; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SPATIAL VARIABILITY; SPATIOTEMPORAL ANALYSIS; TEMPORAL TREND; UNITED STATES; VALIDATION PROCESS; AIR POLLUTANT; AIR POLLUTION; ANALYSIS; CALIFORNIA; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL MONITORING; GOVERNMENT; HUMAN; STATISTICS AND NUMERICAL DATA; WEATHER;

AIR POLLUTANT; PARTICULATE MATTER;

AIR POLLUTANTS; AIR POLLUTION; CALIFORNIA; COHORT STUDIES; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL MONITORING; HUMANS; PARTICULATE MATTER; UNITED STATES; UNITED STATES ENVIRONMENTAL PROTECTION AGENCY; WEATHER;

EID: 85008877378     PISSN: 00916765     EISSN: 15529924     Source Type: Journal    
DOI: 10.1289/EHP131     Document Type: Article

References (51)

1. Alexeeff SE, Schwartz J, Kloog I, Chudnovsky A, Koutrakis P, Coull BA. 2015. Consequences of kriging and land use regression for PM2.5 predic-tions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data. J Expo Sci Environ Epidemiol 25(2):138–144
2. Banerjee S, Carlin BP, Gelfand AE. 2004. Basics of point-referenced data models. In: Hierarchical Modeling and Analysis for Spatial Data. Boca Raton, FL:Chapman and Hall/CRC Press, 21–68
3. 2.5 in the contiguous United States. Environ Sci Technol 47(13):7233–7241
4. Beelen R, Hoek G, van den Brandt PA, Goldbohm RA, Fischer P, Schouten LJ, et al. 2008. Long-term effects of traffic-related air pollution on mortality in a Dutch cohort (NLCS-AIR study). Environ Health Perspect 116:196–202, doi: 10.1289/ehp.10767
5. Beelen R, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, et al. 2014. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet 383(9919):785–795
6. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. 2002. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol 156(9):871–881
7. 2.5 mass concentrations in California, 1980–2007. J Air Waste Manag Assoc 61(3):339–351
8. Brauer M, Amann M, Burnett RT, Cohen A, Dentener F, Ezzati M, et al. 2012. Exposure assessment for estimation of the global burden of disease attribut-able to outdoor air pollution. Environ Sci Technol 46(2):652–660
9. Cesaroni G, Badaloni C, Gariazzo C, Stafoggia M, Sozzi R, Davoli M, et al. 2013. Long-term exposure to urban air pollution and mortality in a cohort of more than a million adults in Rome. Environ Health Perspect 121:324–331, doi: 10.1289/ehp.1205862
10. Chen H, Goldberg MS, Crouse DL, Burnett RT, Jerrett M, Villeneuve PJ, et al. 2010. Back-extrapolation of estimates of exposure from current land-use regression models. Atmos Environ 44:4346–4354
11. coarse in 20 European study areas; results of the ESCAPE project. Environ Sci Technol 46(20):11195–11205
12. Fuentes M, Guttorp P, Sampson PD. 2006. Using transforms to analyze space-time processes. In: Statistical Methods for Spatio-Temporal Systems. Monographs on Statistics and Applied Probability 107 Finkenstädt B, Held L, Isham V, eds. Boca Raton, FL:Chapman and Hall/CRC Press, 77–149
13. 2 land use regression models for historic exposure assessment in Great Britain. Environ Sci Technol 47(14):7804–7811
14. Hand JL, Copeland SA, Day DE, Dillner AM, Indresand H, Malm WC, et al. 2011. Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United States: Report V. June 2011. http://vista.cira.colostate.edu/improve/wp-content/uploads/2016/08/IMPROVE_V_FullReport.pdf [accessed 17 October 2016].
15. Hand JL, Schichtel BA, Malm WC, Copeland S, Molenar JV, Frank N, et al. 2014. Widespread reductions in haze across the United States from the early 1990s through 2011 Atmos Environ 94:671–679
16. 2 in the continental United States, 1985–2000 EnvironHealth Perspect 117:1690–1696, doi: 10.1289/ehp.0900840
17. 2.5 mass and species concen-trations. Atmos Environ 43:2561–2570
18. Hyslop NP. 2009. Impaired visibility: the air pollution people see. Atmos Environ 43:182–195
19. Hystad P, Demers PA, Johnson KC, Brook J, van Donkelaar A, Lamsal L, et al. 2012. Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study. Environ Health 11:22, doi: 10.1186/1476-069X-11-22
20. Kaufman JD, Adar SD, Allen RW, Barr RG, Budoff MJ, Burke GL, et al. 2012. Prospective study of particulate air pollution exposures, subclinical athero-sclerosis, and clinical cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Am J Epidemiol 176(9):825–837
21. Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, et al. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Environ Health Perspect 123:301–309, doi: 10.1289/ehp.1408145
22. 2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos Environ 45:6267–6275
23. 2.5 exposures for health effects assessment. Atmos Envion 38(31):5217–5226
24. Lindström J, Szpiro AA, Sampson PD, Oron AP, Richards M, Larson TV, et al. 2014. A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates. Environ Ecol Stat 21:411–433
25. Malm WC, Schichtel BA, Ames RB, Gebhard KA. 2002. A 10-year spatial and temporal trend of sulfate across the United States. J Geophys Res 107(D22):4627, doi: 10.1029/2002JD002107
26. 2 concentration in Shanghai, China. Environ Res 137:308–315
27. Minnesota Population Center. 2011 National Historical Geographic Information System: Version 2.0. Minneapolis, MN:University of Minnesota. https://www.nhgis.org/documentation/gis-data#years [accessed 26 October 2016].
28. X exposure in a 35-year cohort study. Changes of exposure, and comparison with back extrapolation for historical exposure assess-ment. Atmos Environ 115:62–69
29. Ozkaynak H, Schatz AD, Thurston GD, Isaacs RG, Husar RB. 1985. Relationships between aerosol extinction coefficients derived from airport visual range observations and alternative measures of airborne particle mass. J Air Pollut Control Assoc 35:1176–1185
30. Paciorek CJ, Yanosky JD, Puett RC, Laden F, Suh HH. 2009. Practical large-scale spatio-temporal modeling of particulate matter concentrations. Ann Appl Stat 3(1):370–397
31. Peters JM, Avol E, Berhane K, Gauderman WJ, Gilliland F, Jerrett M, et al. 2004 Epidemiologic Investigation to Identify Chronic Effects of Ambient Air Pollutants in Southern California. California Air Resources Board and the California Environmental Protection Agency. https://www.arb.ca.gov/research/apr/past/94-331a.pdf [accessed 17 October 2016].
32. Puett RC, Hart JE, Yanosky JD, Paciorek C, Schwartz J, Suh H, et al. 2009. Chronic fine and coarse particu-late exposure, mortality, and coronary heart disease in the Nurses’ Health Study. Environ Health Perspect 117:1697–1701, doi: 10.1289/ehp.0900572
33. 2.5 concentrations in epidemiology. Atmos Environ 75:383–392
34. Sampson PD, Szpiro AA, Sheppard L, Lindström J, Kaufman JD. 2011. Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data. Atmos Environ 45:6593–6606
35. Szpiro AA, Paciorek CJ. 2013. Measurement error in two-stage analyses, with application to air pollution epidemiology. Environmetrics 24:501–517
36. Szpiro AA, Paciorek CJ, Sheppard L. 2011a. Does more accurate exposure prediction necessarily improve health effect estimates? Epidemiology 22(5):680–685
37. Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar S, Kaufman JD. 2009. Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics 21:606–631
38. Szpiro AA, Sheppard L, Lumley T. 2011b. Efficient measurement error correction with spatially misaligned data. Biostatistics 12(4):610–623
39. Hinton DO, Sune JM, Suggs JC, Barnard WF. 1985 Inhalable Particulate Network Report: Operation and Data Summary (Mass Concentrations Only). EPA 600/S4-84-088. Washington, DC:U.S. EPA.
40. U.S. EPA (U.S. Environmental Protection Agency). 2004a. The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003. EPA 454-R-04-002. Washington, DC:U.S. EPA. http://nepis.epa.gov/Adobe/PDF/30005VAS.pdf [accessed 26 October 2016].
41. U.S. EPA. 2004b. Air Quality Criteria for Particulate Matter (Final Report, Oct 24). Volume 1. EPA 600/P-99/002aF-bF. Washington, DC:U.S. EPA.
42. U.S. EPA. 2005. Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment of Scientific and Technical Information. OAQPS Staff Paper. EPA 452-R-05- 005a. Research Triangle Park, NC:U.S. EPA.
43. U.S. EPA. 2015. CASTNET Factsheet. Washington, DC:U.S. EPA. https://www3.epa.gov/castnet/docs/CASTNET-Factsheet-2015.pdf [accessed 17 October 2016].
44. U.S. EPA. 2014. AirData. Download Data Files. http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html [accessed 17 October 2016]
45. Wang R, Henderson SB, Sbihi H, Allen RW, Brauer M. 2013. Temporal stability of land use regression models for traffic-related air pollution. Atmos Environ 64:312–319
46. Weichenthal S, Villeneuve PJ, Burnett RT, van Donkelaar A, Martin RV, Jones RR, et al. 2014. Long- term exposure to fine particulate matter: association with nonaccidental and cardiovascular mortality in the Agricultural Health Study cohort. Environ Health Perspect 122:609–615, doi: 10.1289/ehp.1307277
47. Xing J, Pleim J, Mathur R, Pouliot G, Hogrefe C, Gan CM, et al. 2013. Historical gaseous and primary aerosol emissions in the United States from 1990 to 2010 Atmos Chem Phys 13:7531–7549
48. Yanosky JD, Paciorek CJ, Laden F, Hart JE, Puett RC, Liao D, et al. 2014. Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors. Environ Health 13:63, doi: 10.1186/1476-069X-13-63
49. Yanosky JD, Paciorek CJ, Suh HH. 2009. Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States. Environ Health Perspect 117:522–529, doi: 10.1289/ehp.11692
50. Young MT, Sandler DP, DeRoo LA, Vedal S, Kaufman JD, London SJ. 2014. Ambient air pollution exposure and incident adult asthma in a nationwide cohort of U.S. women. Am J Respir Crit Care Med 15:190(8):914–921
51. Yu HL, Wang CH. 2010. Retrospective prediction of intraurban spatio temporal distribution of PM2.5 in Taipei. Atmos Environ 44:3053–3065.