David M. Holland

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Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but(More)
Over the past few years, Bayesian models for combining output from numerical models and air monitoring data have been applied to environmental data sets to improve spatial prediction. This paper develops a new hierarchical Bayesian model (HBM) for fine particulate matter (PM2.5) that combines U. S. EPA Federal Reference Method (FRM) PM2.5 monitoring data(More)
In recent decades, Antarctica has experienced pronounced climate changes. The Antarctic Peninsula exhibited the strongest warming of any region on the planet, causing rapid changes in land ice. Additionally, in contrast to the sea-ice decline over the Arctic, Antarctic sea ice has not declined, but has instead undergone a perplexing redistribution.(More)
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(More)
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 proposes a two-stage approach for the estimation of regional trends(More)
The assessment of air pollution regulatory programs designed to improve ground level ozone concentrations is a topic of considerable interest to environmental managers. To aid this assessment, it is necessary to model the space-time behavior of ozone for predicting summaries of ozone across spatial domains of interest and for the detection of long-term(More)
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.,(More)
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(More)
We used a terrestrial radar interferometer (TRI) at Helheim Glacier, Greenland, in August 2013, to study the effects of tidal forcing on the terminal zone of this tidewater glacier. During our study period, the glacier velocity was up to 25md. Our measurements show that the glacier moves out of phase with the semi-diurnal tides and the densely packed(More)