Predicting intra‐urban variation in air pollution concentrations with complex spatio‐temporal dependencies

Abstract

We describe a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatio-temporal correlation structure and can accommodate spatio-temporally misaligned observations. This methodology has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the U.S. EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space-time field into a “mean” that includes dependence on covariates and spatially varying seasonal and long-term trends and a “residual” that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio-temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single-stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long-term average concentrations of oxides of nitrogen (NOx) from 2005–2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross-validated R2 is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long-term average concentrations.

Consequences of large-scale salinity alteration during the Deepwater Horizon oil spill on subtidal oyster populations

“…Daily average salinities and temperatures for 2006 through 2012 were estimated on a 200 × 200 m gridcell basis using a spatio-temporal kriging model (Szpiro et al 2009, Lindstrom et al 2011 (Fig. 4).…”

Elucidating multipollutant exposure across a complex metropolitan area by systematic deployment of a mobile laboratory

“…These "saturation monitoring" data sets can then serve as the source of the dependent variable in empirical exposure model development, such as land use regression (LUR) (e.g., Brauer et al, 2003;Henderson et al, 2007;Johnson et al, 2010;Kanaroglou et al, 2005). These approaches may also include data sets collected sequentially to increase the overall number of locations with active sampling for measurement of particles, although this requires temporal adjustments (e.g., Cyrys et al, 2005;Eeftens et al, 2012;Hochadel et al, 2006;Wang et al, 2013) or more complex spatial-temporal modeling methodologies (Gryparis et al, 2007;Szpiro et al, 2010) in the model development. Since the magnitude of the temporal adjustment can also vary spatially and is not well characterized, this can result in additional uncertainty in estimates of the long-term spatial patterns.…”

Traffic-related Air Pollution and the Right Ventricle. The Multi-ethnic Study of Atherosclerosis

“…Using weighted averages of residential addresses over the year prior to cardiac MRI, individual outdoor home exposure to NO 2 and NO x was estimated using spatiotemporal modeling and maximized by maximum likelihood (Figure 1) (16,17). Estimates were fit using monitoring data from the Environmental Protection Agencies Air Quality System database and extensive cohort-specific air monitoring including home-based monitoring conducted as part of MESA Air (18).…”