Spatiotemporal modeling with temporal-invariant variogram subgroups to estimate fine particulate matter PM2.5 concentrations

Abstract Short-term exposure estimation of daily air pollution levels incorporating geographic information system (GIS) into spatiotemporal modeling remains a great challenge for assessing corresponding acute adverse health effects. Due to daily meteorological effects on the dispersion of pollutants, explanatory spatial covariables and their coefficients may not be the same as in classical land-use regression (LUR) modeling for long-term exposure. In this paper, we propose a two-stage spatiotemporal model for daily fine particulate matter (PM2.5) concentration prediction: first, daily nonlinear temporal trends are estimated through a generalized additive model, and second, GIS covariates are used to predict spatial variation in the temporal trend-removed residuals. To account for spatial dependence on meteorological conditions, the dates of the study period are divided by the sill of the daily empirical variogram into approximately temporal-invariant subgroups. Within each subgroup, daily PM2.5 estimations are obtained by combining the temporal and spatial parts of the estimations from the two stages. The proposed method is applied to the modeling of spatiotemporal PM2.5 concentrations observed at 18 ambient air monitoring stations in Taipei metropolitan area during 2006–2008. The results showed that the PM2.5 concentrations decreased whereas the relative humidity and wind speed increased with the sill subgroups, which may be due to the effects of daily meteorological conditions on the dispersions of the particles. Also, the covariates and their coefficients of the LUR models varied with subgroups and had in general higher adjusted R-squares and smaller root mean square errors in prediction than those of a single overall LUR model.

[1]  J. Schwartz,et al.  The Effect of Fine and Coarse Particulate Air Pollution on Mortality: A National Analysis , 2009, Environmental health perspectives.

[2]  Peter J. Diggle,et al.  Modelling spatio‐temporal variation in exposure to particulate matter: a two‐stage approach , 2008 .

[3]  Chang-Chuan Chan,et al.  Increasing cardiopulmonary emergency visits by long-range transported Asian dust storms in Taiwan. , 2008, Environmental research.

[4]  Kazuhiko Ito,et al.  A land use regression for predicting fine particulate matter concentrations in the New York City region , 2007 .

[5]  J. Pearce,et al.  A review of intraurban variations in particulate air pollution: Implications for epidemiological research , 2005 .

[6]  R. Beelen,et al.  Comparison of land-use regression models between Great Britain and the Netherlands , 2010 .

[7]  Patrick Bogaert,et al.  Spatiotemporal modelling of ozone distribution in the State of California , 2009 .

[8]  P. Vokonas,et al.  Medium-Term Exposure to Traffic-Related Air Pollution and Markers of Inflammation and Endothelial Function , 2011, Environmental health perspectives.

[9]  Jing-Shiang Hwang,et al.  Effects of concentrated ambient particles on heart rate and blood pressure in pulmonary hypertensive rats. , 2002, Environmental health perspectives.

[10]  J Schwartz,et al.  The effects of particulate air pollution on daily deaths: a multi-city case crossover analysis , 2004, Occupational and Environmental Medicine.

[11]  P. Diggle,et al.  Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale , 2011 .

[12]  G. Lemasters,et al.  A Review of Land-use Regression Models for Characterizing Intraurban Air Pollution Exposure , 2007, Inhalation toxicology.

[13]  Jonathan I Levy,et al.  Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants , 2008, Environmental health : a global access science source.

[14]  Joel Schwartz,et al.  Mortality Risk Associated with Short-Term Exposure to Traffic Particles and Sulfates , 2007, Environmental health perspectives.

[15]  Hwa-Lung Yu,et al.  Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods , 2011, International journal of environmental research and public health.

[16]  Christopher J. Paciorek,et al.  Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States , 2008, Environmental health perspectives.

[17]  Yu Hwa-Lung,et al.  Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei , 2010 .

[18]  Thomas Lumley,et al.  Predicting intra‐urban variation in air pollution concentrations with complex spatio‐temporal dependencies , 2009, Environmetrics.

[19]  J. Schwartz,et al.  Semiparametric latent variable regression models for spatiotemporal modelling of mobile source particles in the greater Boston area , 2007 .

[20]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .

[21]  Chang-Chuan Chan,et al.  Effects of Particle Size Fractions on Reducing Heart Rate Variability in Cardiac and Hypertensive Patients , 2005, Environmental health perspectives.

[22]  Michael Brauer,et al.  Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. , 2007, Environmental science & technology.

[23]  Altaf Arain,et al.  A review and evaluation of intraurban air pollution exposure models , 2005, Journal of Exposure Analysis and Environmental Epidemiology.

[24]  J. Gulliver,et al.  A review of land-use regression models to assess spatial variation of outdoor air pollution , 2008 .