David M. Holland

High Resolution Space-Time Ozone Modeling for Assessing Trends.

This article proposes a space–time model for daily 8-hour maximum ozone levels to provide input for regulatory activities: detection, evaluation, and analysis of spatial patterns and temporal trend in ozone summaries. The model is applied to the analysis of data from the state of Ohio that contains a mix of urban, suburban, and rural ozone monitoring sites. The proposed space–time model is autoregressive and incorporates the most important meteorological variables observed at a collection of ozone monitoring sites as well as at several weather stations where ozone levels have not been observed. This misalignment is handled through spatial modeling. In so doing we adopt a computationally convenient approach based on the successive daily increments in meteorological variables. The resulting hierarchical model is specified within a Bayesian framework and is fitted using Markov chain Monte Carlo techniques. Full inference with regard to model unknowns as well as for predictions in time and space, evaluation of annual summaries, and assessment of trends are presented.

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.

Spatio-temporal modeling of fine particulate matter

Studies indicate that even short-term exposure to high concentrations of fine atmospheric particulate matter (PM2.5) can lead to long-term health effects. In this article, we propose a random effects model for PM2.5 concentrations. In particular, we anticipate urban/rural differences with regard to both mean levels and variability. Hence we introduce two random effects components, one for rural or background levels and the other as a supplement for urban areas. These are specified in the form of spatio-temporal processes. Weighting these processes through a population density surface results in nonstationarity in space. We analyze daily PM2.5 concentrations in three midwestern U.S. states for the year 2001. A fully Bayesian model is implemented, using MCMC techniques, which enables full inference with regard to process unknowns as well as predictions in time and space.

Trends in atmospheric sulfur and nitrogen species in the eastern United States for 1989-1995

Emission reductions were mandated in the Clean Air Act Amendments of 1990 with the expectation that they would result in major reductions in the concentrations of atmospherically transported pollutants. This paper investigates the form and magnitude of trends from 1989 to 1995 in atmospheric concentrations of sulfur dioxide, sulfate, and nitrogen at 34 rural sites in the eastern US. Across all sites, there is strong evidence of statistically significant declining trends in sulfur dioxide (median change of -35%) and sulfate concentrations (median change of -26%). In general, trends in nitrogen concentrations were not as pronounced (median change of -8%) as trends in the sulfur compounds. A regional estimate of trend for a cluster of sites in the Ohio River valley showed close correspondence between declining sulfur dioxide concentrations (-35%) and changes in sulfur dioxide emissions (-32%) in this region.

Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality

We provide methods that can be used to obtain more accurate environmental exposure assessment. In particular, we propose two modeling approaches to combine monitoring data at point level with numerical model output at grid cell level, yielding improved prediction of ambient exposure at point level. Extending our earlier downscaler model (Berrocal, V. J., Gelfand, A. E., and Holland, D. M. (2010b). A spatio-temporal downscaler for outputs from numerical models. Journal of Agricultural, Biological and Environmental Statistics 15, 176-197), these new models are intended to address two potential concerns with the model output. One recognizes that there may be useful information in the outputs for grid cells that are neighbors of the one in which the location lies. The second acknowledges potential spatial misalignment between a station and its putatively associated grid cell. The first model is a Gaussian Markov random field smoothed downscaler that relates monitoring station data and computer model output via the introduction of a latent Gaussian Markov random field linked to both sources of data. The second model is a smoothed downscaler with spatially varying random weights defined through a latent Gaussian process and an exponential kernel function, that yields, at each site, a new variable on which the monitoring station data is regressed with a spatial linear model. We applied both methods to daily ozone concentration data for the Eastern US during the summer months of June, July and August 2001, obtaining, respectively, a 5% and a 15% predictive gain in overall predictive mean square error over our earlier downscaler model (Berrocal et al., 2010b). Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites.

Variations of NO, NO2 and O3 concentrations downwind of a Los Angeles freeway:

Abstract A sampling study was conducted to quantify the relationships of NO, NO2 and O3 concentrations with distance downwind of the San Diego freeway in Los Angeles. By continuously, monitoring at a site upwind (background) of the freeway and at selected downwind sites, patterns of NO, NO2 and O3 concentrations were detailed. Minimal separation distances of the samplers from the roadway to eliminate measurable influence were estimated to be approx 400–500 m for NO, NO2 and O3. A spatial model was fitted to the empirical NO and NO2 data, which incorporated the effects of dilution, reaction and background level on measured downwind concentration. This model fit the experimental data closely and indicated that: 1. (1) the decrease of NOx downwind of the freeway can be expressed by a simple exponential dilution, 2. (2) the reaction of NO and O3 downwind of the freeway is not well mixed and deviates from the ideal photostationary state, and 3. (3) the simple rate equations for the reaction of NO with O3 and the photodissociation of NO2 could be combined with a simple exponential dilution term to define the measured concentrations of NO2.

A bivariate space-time downscaler under space and time misalignment.

Ozone and particulate matter PM(2.5) are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce concentration surfaces over large spatial regions. In this paper, we offer a fully-model based approach for fusing these two sources of information for the pair of co-pollutants which is computationally feasible over large spatial regions and long periods of time. Due to the association between concentration levels of the two environmental contaminants, it is expected that information regarding one will help to improve prediction of the other. Misalignment is an obvious issue since the monitoring networks for the two contaminants only partly intersect and because the collection rate for PM(2.5) is typically less frequent than that for ozone.Extending previous work in Berrocal et al. (2009), we introduce a bivariate downscaler that provides a flexible class of bivariate space-time assimilation models. We discuss computational issues for model fitting and analyze a dataset for ozone and PM(2.5) for the ozone season during year 2002. We show a modest improvement in predictive performance, not surprising in a setting where we can anticipate only a small gain.

Fitting statistical distributions to air quality data by the maximum likelihood method

Abstract A computer program has been developed for fitting statistical distributions to air pollution data using maximum likelihood estimation. Appropriate uses of this software are discussed and a grouped data example is presented. The program fits the following continuous distributions: normal, three-parameter lognormal, three-parameter gamma, three-parameter Weibull, Johnson SB, and four-parameter beta. The parameters of each distribution are estimated by closed solutions or the Nelder-Mead Simplex iterative search. Graphical output contains a plot of the fitted distribution superimposed upon the histogram of the data for each model. Six goodness-of-fit criteria are supplied and ranked by the program to aid in the selection of the most appropriate choice among the six models.

Estimation of regional trends in sulfur dioxide over the eastern United States

Emission reductions were mandated in the Clean Air Act Amendments of 1990 with the expectation of concomitant reductions in ambient concentrations of atmospherically-transported pollutants. To evaluate the effectiveness of the legislated emission reductions using monitoring data, this paper proposed a two-stage approach for the estimation of regional trends and their standard errors. In the first stage, a generalized model (GAM) is fitted to airborne sulfur dioxide (SO2) data at each of 35 sites in the eastern United States to estimate the form and magnitude of the site-specific trend (defined as percent total change) from 1989 to 1995. This analysis is designed to adjust the SO2 data for the influences of meteorology and season. In the second stage, the estimated trends are treated as samples with site-dependent measurement error from a Gaussian random field with a stationary covariance function. Kriging methodology is adapted to construct spatially-smoothed estimates of the true trend for three large regions in the eastern U.S. Finally, a Bayesian analysis with Markov Chain Monte Carlo (MCMC) methods is used to obtain regional trend estimates and their standard errors, which take account of the estimation of the unknown covariance parameters as well as the stochastic variation of the random fields. Both spatial estimation techniques produced similar results in terms of regional trend and standard error. Copyright

A Field Comparison of PM10 Inlets at Four Locations

A comprehensive field study was conducted comparing the performance of PM10 inlets under a variety of field conditions. Inlets for low flow, medium flow, and high flow samplers were evaluated at four sampling locations providing a range of concentrations and particle sizes. Sampler precisions were determined at each location along with the regression parameter estimates comparing sampler types. The daily distributions of mass by aerodynamic diameter were measured using the Wide Range Aerosol Classifier and the composited data reported. The expected mass concentrations were calculated using the sampler effectiveness data and the size distributions and then compared to the measured values. This permitted examination of the utility of the proposed Federal Reference Method (FRM) approach for specifying PM10 samplers. The comparison results indicate that the precisions of the PM10 samplers are well within the FRM requirements. The performance of the inlets as characterized by wind tunnel testing provided compu...