A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution

Environmental Health Perspectives

Volumn 123, Issue 4, 2015, Pages 301-309

  Keller, Joshua P ^a   Olives, Casey ^a   Kim, Sun Young ^a,b   Sheppard, Lianne ^a   Sampson, Paul D ^a   Szpiro, Adam A ^a   Oron, Assaf P ^c   Lindström, Johan ^d   Vedal, Sverre ^a   Kaufman, Joel D ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Seoul National University   (South Korea)

^c SEATTLE CHILDREN S HOSPITAL   (United States)

^d LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

BLACK CARBON; NITROGEN DIOXIDE; AIR POLLUTANT; CARBON; NITROGEN OXIDE; PARTICULATE MATTER;

AIR POLLUTION; ARTICLE; ATHEROSCLEROSIS; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL MONITORING; GEOGRAPHIC DISTRIBUTION; LIMIT OF DETECTION; LONG TERM EXPOSURE; MEASUREMENT ACCURACY; PARTICULATE MATTER; POLLUTANT; PREDICTION; PRIORITY JOURNAL; SCORING SYSTEM; SPATIOTEMPORAL ANALYSIS; VALIDATION PROCESS; AIR POLLUTANT; ANALYSIS; ETHNIC GROUP; ETHNOLOGY; HUMAN; METABOLISM; PROCEDURES; THEORETICAL MODEL; UNITED STATES;

AIR POLLUTANTS; AIR POLLUTION; ATHEROSCLEROSIS; CARBON; ENVIRONMENTAL MONITORING; ETHNIC GROUPS; HUMANS; MODELS, THEORETICAL; NITROGEN OXIDES; PARTICULATE MATTER; SPATIO-TEMPORAL ANALYSIS; UNITED STATES;

EID: 84961306016     PISSN: 00916765     EISSN: 15529924     Source Type: Journal    
DOI: 10.1289/ehp.1408145     Document Type: Article

References (27)

1. Abdi H. 2010. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat 2:97–106; doi:10.1002/wics.–51.
2. Allen RW, Amram O, Wheeler AJ, Brauer M. 2011. The transferability of NO and NO2 land use regression models between cities and pollutants. Atmos Environ 45:369–378; doi:10.1016/j.atmosenv.2010.10.–002.
3. Beckerman BS, Jerrett M, Serre M, Martin RV, Lee SJ, van Donkelaar A, et al. 2013. A hybrid approach to estimating national scale spatiotemporal vari-ability of PM2.5 in the contiguous United States. Environ Sci Technol 47:7233–7241; doi:10.1021/es400039u.
4. Beelen R, Hoek G, Pebesma E, Vienneau D, de Hoogh K, Briggs DJ. 2009. Mapping of background air pollution at a fine spatial scale across the European Union. Sci Total Environ 407:1852–1867; doi:10.1016/j.scitotenv.2008.11.–048.
5. Bergen S, Sheppard L, Sampson PD, Kim SY, Richards M, Vedal S, et al. 2013. A national predic tion model for PM2.5 component exposures and measurement error-corrected health effect inference. Environ Health Perspect 121:1017–1025; doi:. 10.1289/ehp.–1206010
6. Brook RD, Rajagopalan S, Pope CA III, Brook JR, Bhatnagar A, Diez-Roux AV, et al. 2010. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation 121:2331–2378; doi:10.1161/CIR.0b013e3181dbece–1.
7. Cohen MA, Adar SD, Allen RW, Avol E, Curl CL, Gould T, et al. 2009. Approach to estimating partic-ipant pollutant exposures in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Environ Sci Technol 43:4687–4693.
8. Cressie NAC. 1993. Statistics for Spatial Data, Revised Edition. Hoboken NJ:John Wiley & Sons.
9. Eckhoff P, Braverman T. 1995. Addendum to the User’s Guide to CAL3QHC Version 2.0 (CAL3QHCR user’s guide). Available: http://www.epa.gov/scram001/userg/regmod/cal3qhcrug.pdf [accessed 4 March 2015].
10. Franklin M, Vora H, Avol E, McConnell R, Lurmann F, Liu F, et al. 2012. Predictors of intra-community variation in air quality. J Expo Sci Environ Epidemiol 22:135–147; doi:10.1038/jes.2011–45.
11. Fuentes M, Guttorp P, Sampson PD. 2007. Using transforms to analyze space-time processes. In: Statistical Methods for Spatio-Temporal Systems (Finkenstädt B, Held L, Isham V, eds.). Boca Raton, FL:Chapman Hall/CRC, 77–150.
12. Hart JE, Yanosky JD, Puett RC, Ryan L, Dockery DW, Smith TJ, et al. 2009. Spatial modeling of PM10 and NO2 in the continental United States, 1985–2000. Environ Health Perspect 117:1690–1696; doi:10.1289/ehp.–0900840.
13. Hoek G, Beelen R, de Hoogh K, Vienneau D, Gulliver J, Fischer P, et al. 2008. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ 42:7561–7578; doi:10.1016/j.atmosenv.2008.05.–057.
14. Jerrett M, Arain M, Kanaroglou P, Beckerman B, Crouse D, Gilbert NL, et al. 2007. Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada. J Toxicol Environ Health A 70:200–212; doi:10.1080/–15287390600883018.
15. Kaufman JD, Adar SD, Allen RW, Barr RG, Budoff MJ, Burke GL, et al. 2012. Prospective study of particu-late air pollution exposures, subclinical athero-sclerosis, and clinical cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Am J Epidemiol 176:825–837; doi:10.1093/aje/kws–169.
16. Kloog I, Koutrakis P, Coull BA, Lee HJ, Schwartz J. 2011. Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measure-ments. Atmos Environ 45:6267–6275; doi:10.1016/j.atmosenv.2011.08.–066.
17. Lindström J, Szpiro AA, Sampson PD, Bergen S, Oron AP. 2012. SpatioTemporal: Spatio- Temporal Model Estimation. R Package Version 1.1.7. Available: http://cran.r-project.org/web/packages/SpatioTemporal/index.html [accessed 1 September 2013].
18. Lindström J, Szpiro AA, Sampson PD, Oron AP, Richards M, Larson TV, et al. 2014. A flexible spatio-temporal model for air pollution with spatio-temporal covariates. Environ Ecol Stat 21:411–433; doi:10.1007/s10651-013-0261-4.
19. Matte TD, Ross Z, Kheirbek I, Eisl H, Johnson S, Gorczynski JE, et al. 2013. Monitoring intraurban spatial patterns of multiple combustion air pollut-ants in New York City: design and implementa-tion. J Expo Sci Environ Epidemiol 23:223–231; doi:10.1038/jes.2012.–126.
20. Mercer LD, Szpiro AA, Sheppard L, Lindström J, Adar SD, Allen RW, et al. 2011. Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Atmos Environ 45:4412–4420; doi:10.1016/j.atmosenv.2011.05.–043.
21. Mevik BH, Wehrens R, Liland KH. 2011. PLS: Partial Least Squares and Principal Component regression. R Package Version 2.3-0. Available: http://cran.r-project.org/web/packages/pls/index.html. [accessed 1 October 2012].
22. Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, et al. 2007. Long- term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med 356:447–458.
23. NYC Department of Health. 2014. NYC Community Air Survey. Available: http://www.nyc.gov/health/nyccas [accessed 13 January 2014].
24. Pope CA III, Dockery DW. 2006. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc 56:709–742; doi:10.1080/10473289.2006.–10464485.
25. Sampson PD, Richards M, Szpiro AA, Bergen S, Sheppard L, Larson TV, et al. 2013. A regionalized national universal kriging model using partial least squares regression for estimating annual PM2.5 concentrations in epidemiology. Atmos Environ 75:383–392; doi:10.1016/j.atmosenv.2013.04.–015.
26. 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; doi:10.1016/j.atmosenv.2011.04.–073.
27. Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar SD, Kaufman JD. 2010. Predicting intra- urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics 21:606–631; doi:10.1002/env.1014.