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Topics in Bayesian Spatiotemporal Prediction of Environmental Exposure

dc.contributor.advisor Gelfand, Alan E White, Philip Andrew 2019-06-07T19:48:09Z 2019-06-07T19:48:09Z 2019
dc.description Dissertation
dc.description.abstract <p>We address predictive modeling for spatial and spatiotemporal modeling in a variety of settings. First, we discuss spatial and spatiotemporal data and corresponding model types used in later chapters. Specifically, we discuss Markov random fields, Gaussian processes, and Bayesian inference. Then, we outline the dissertation.</p><p>In Chapter 2, we consider the setting where areal unit data are only partially observed. First, we consider setting where a portion of the areal units have been observed, and we seek prediction of the remainder. Second, we leverage these ideas for model comparison where we fit models of interest to a portion of the data and hold out the rest for model comparison.</p><p>In Chapters 3 and 4, we consider pollution data from Mexico City in 2017. In Chapter 3 we forecast pollution emergencies. Mexico City defines pollution emergencies using thresholds that rely on regional maxima for ozone and for particulate matter with diameter less than 10 micrometers (PM10). To predict local pollution emergencies and to assess compliance with Mexican ambient air quality standards, we analyze hourly ozone and PM10 measurements from 24 stations across Mexico City from 2017 using a bivariate spatiotemporal model. With this model, we predict future pollutant levels using current weather conditions and recent pollutant concentrations. Employing hourly pollutant projections, we predict regional maxima needed to estimate the probability of future pollution emergencies. We discuss how predicted compliance with legislated pollution limits varies across regions within Mexico City in 2017.</p><p>In Chapter 4, we propose a continuous spatiotemporal model for Mexico City ozone levels that accounts for distinct daily seasonality, as well as variation across the city and over the peak ozone season (April and May) of 2017. To account for these patterns, we use covariance models over space, circles, and time. We review relevant existing covariance models and develop new classes of nonseparable covariance models appropriate for seasonal data collected at many locations. We compare the predictive performance of a variety of models that utilize various nonseparable covariance functions. We use the best model to predict hourly ozone levels at unmonitored locations in April and May to infer compliance with Mexican air quality standards and to estimate respiratory health risk associated with ozone exposure.</p>
dc.subject Statistics
dc.subject Environmental health
dc.subject Bayesian statistics
dc.subject Gaussian process
dc.subject Markov random field
dc.subject nonseparable covariance
dc.subject pollution monitoring
dc.subject spatial statistics
dc.title Topics in Bayesian Spatiotemporal Prediction of Environmental Exposure
dc.type Dissertation
dc.department Statistical Science

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