A data fusion approach for spatial analysis of speciated PM2.5 across time

PM2.5 exposure is linked to a number of adverse health effects such as lung cancer and cardiovascular disease. However, PM2.5 is a complex mixture of different species whose composition varies substantially in both space and time. An open question is how these constituent species contribute to the overall negative health outcomes seen from PM2.5 exposure. To this end, the Environmental Protection Agency as well as other federal, state, and local organization monitor total PM2.5 along with its primary species on a national scale. From an epidemiological perspective, there is a need to develop effective methods that will allow for the spatially and temporally sparse observations to be used to predict exposures for locations across the entire United States.

[1]  A. Gelfand,et al.  A Bayesian coregionalization approach for multivariate pollutant data , 2003 .

[2]  C. F. Sirmans,et al.  Nonstationary multivariate process modeling through spatially varying coregionalization , 2004 .

[3]  Montserrat Fuentes,et al.  Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models , 2005, Biometrics.

[4]  Kazuhiko Ito,et al.  Cardiovascular Effects of Nickel in Ambient Air , 2006, Environmental health perspectives.

[5]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[6]  F. Dominici,et al.  Does the Effect of PM10 on Mortality Depend on PM Nickel and Vanadium Content? A Reanalysis of the NMMAPS Data , 2007, Environmental health perspectives.

[7]  Yuhang Wang,et al.  Statistical correction and downscaling of chemical transport model ozone forecasts over Atlanta , 2008 .

[8]  Catherine A. Calder,et al.  A dynamic process convolution approach to modeling ambient particulate matter concentrations , 2008 .

[9]  James V. Zidek,et al.  Combining Measurements and Physical Model Outputs for the Spatial Prediction of Hourly Ozone Space-Time Fields ∗ , 2008 .

[10]  Petros Koutrakis,et al.  The Role of Particle Composition on the Association Between PM2.5 and Mortality , 2008, Epidemiology.

[11]  Jingyu Feng,et al.  Combining numerical model output and particulate data using Bayesian space–time modeling , 2009 .

[12]  Jerry M. Davis,et al.  Multivariate Spatial-temporal Modeling and Prediction of Speciated Fine Particles , 2009, Journal of statistical theory and practice.

[13]  Jeffrey Young,et al.  Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 , 2009 .

[14]  Alan E Gelfand,et al.  A Spatio-Temporal Downscaler for Output From Numerical Models , 2010, Journal of agricultural, biological, and environmental statistics.

[15]  A. Gelfand,et al.  A bivariate space-time downscaler under space and time misalignment. , 2010, The annals of applied statistics.

[16]  Matti Vihola,et al.  Robust adaptive Metropolis algorithm with coerced acceptance rate , 2010, Statistics and Computing.

[17]  Alan E Gelfand,et al.  Space‐Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality , 2012, Biometrics.

[18]  J. Burke,et al.  The triggering of myocardial infarction by fine particles is enhanced when particles are enriched in secondary species. , 2013, Environmental science & technology.

[19]  L. Sheppard,et al.  Issues Related to Combining Multiple Speciated PM2.5 Data Sources in Spatio-Temporal Exposure Models for Epidemiology: The NPACT Case Study , 2013 .

[20]  Kazuhiko Ito,et al.  National Particle Component Toxicity (NPACT) Initiative: integrated epidemiologic and toxicologic studies of the health effects of particulate matter components. , 2013, Research report.

[21]  F. Dominici,et al.  Associations of PM2.5 Constituents and Sources with Hospital Admissions: Analysis of Four Counties in Connecticut and Massachusetts (USA) for Persons ≥ 65 Years of Age , 2013, Environmental health perspectives.