Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data

Journal of Exposure Science and Environmental Epidemiology

Volumn 25, Issue 2, 2015, Pages 138-144

  Alexeeff, Stacey E ^a,b   Schwartz, Joel ^a   Kloog, Itai ^a,c   Chudnovsky, Alexandra ^a   Koutrakis, Petros ^a   Coull, Brent A ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^c BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

air pollution; kriging; land use regression; measurement error; PM2.5; spatial models

Indexed keywords

PARTICULATE MATTER;

ADVERSE EFFECTS; AIR POLLUTION; ALGORITHM; ANALYSIS; COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; HUMAN; PARTICLE SIZE; PARTICULATE MATTER; PROCEDURES; SPACE FLIGHT; SPATIAL ANALYSIS; STANDARDS; STATISTICAL BIAS; STATISTICAL MODEL; UNITED STATES;

AIR POLLUTION; ALGORITHMS; BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; HUMANS; LINEAR MODELS; LOGISTIC MODELS; NEW ENGLAND; PARTICLE SIZE; PARTICULATE MATTER; SPACECRAFT; SPATIAL ANALYSIS;

EID: 84923544390     PISSN: 15590631     EISSN: 1559064X     Source Type: Journal    
DOI: 10.1038/jes.2014.40     Document Type: Article

References (22)

1. Brook RD, Rajagopalan S, Pope III CA, Brook JR, Bhatnagar A, Diez-Roux AV et al. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation 2010; 121: 2331-2378.
2. Brauer M, Hoek G, van Vliet P, Meliefste K, Fischer P, Gehring U et al. Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems. Epidemiology 2003; 14: 228-239.
3. Clougherty JE, Wright RJ, Baxter LK, Levy JI. Land use regression modeling of intraurban residential variability in multiple traffic-related air pollutants. Environ Health 2008; 7: 17.
4. Liao D, Peuquet DJ, Duan Y, Whitsel EA, Dou J, Smith RL et al. GIS approaches for the estimation of residential-level ambient PM concentrations. Environ Health Persp 2006; 114: 1374-1380.
5. Jerrett M, Burnett RT, Ma R, Pope III CA, Krewski D, Newbold KB et al. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 2005; 16: 727-736.
6. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu C. Measurement Error in Nonlinear Models: A Modern Perspective. Chapman and Hall/CRC, 2006.
7. Gryparis A, Paciorek CJ, Zeka A, Schwartz J, Coull BA. Measurement error caused by spatial misalignment in environmental epidemiology. Biostatistics 2009; 10: 258-274.
8. Lopiano KK, Young LJ, Gotway CA. A comparison of errors in variables methods for use in regression models with spatially misaligned data. Stat Methods Med Res 2011; 20: 29-47.
9. Madsen L, Ruppert D, Altman NS. Regression with spatially misaligned data. Environmetrics 2008; 19: 453-467.
10. Szpiro AA, Sheppard L, Lumley T. Efficient measurement error correction with spatially misaligned data. Biostatistics 2011; 12: 610-623.
11. Remer LA, Kaufman YJ, Tanre D, Mattoo S, Chu DA, Martins JV et al. The MODIS aerosol algorithm, products, and validation. J Atmos Sci 2005; 62: 947-973.
12. Kloog I, Koutrakis P, Coull BA, Lee HJ, Schwartz J. Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos Environ 2011; 45: 6267-6275.
13. Kloog I, Nordio F, Coull BA, Schwartz J. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM(2.5) exposures in the Mid-Atlantic states. Enviro Sci Technol 2012; 46: 11913-11921.
14. Lyapustin A, Martonchik J, Wang YJ, Laszlo I, Korkin S. Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables. J Geophys Res Atmos 2011; 116: D03210.
15. Lyapustin A, Wang Y, Laszlo I, Kahn R, Korkin S, Remer L et al. Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. J Geophys Res Atmos 2011; 116: D03211.
16. Lyapustin A, Wang Y, Frey R. An automatic cloud mask algorithm based on time series of MODIS measurements. J Geophys Res Atmos 2008; 113: D16207.
17. Chudnovsky A, Tang C, Lyapustin A, Wang Y, Schwartz J, Koutrakis P. A critical assessment of high resolution aerosol optical depth (AOD) retrievals for fine particulate matter (PM) predictions. Atmos Chem Phys Discuss 2013; 13: 14581-14611.
18. Kloog I, Melly SJ, Ridgway WL, Coull BA, Schwartz J. Using new satellite based exposure methods to study the association between pregnancy PM(2).(5) exposure, premature birth and birth weight in Massachusetts. Environ Health 2012; 11: 40.
19. Szpiro AA, Paciorek CJ, Sheppard L. Does more accurate exposure prediction necessarily improve health effect estimates? Epidemiology 2011; 22: 680-685.
20. Basagana X, Aguilera I, Rivera M, Agis D, Foraster M, Marrugat J et al. Measurement error in epidemiologic studies of air pollution based on land-use regression models. Am J Epidemiol 2013; 178: 1342-1346.
21. Szpiro AA, Paciorek CJ. Measurement error in two-stage analyses, with application to air pollution epidemiology. Environmetrics 2013; 24: 501-517.
22. Zeger SL, Thomas D, Dominici F, Samet JM, Schwartz J, Dockery D et al. Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environ Health Persp 2000; 108: 419-426.