A general theory for preferential sampling in environmental networks

This is the final version. Available from the Institute of Mathematical Statistics via the DOI in this recordThis paper presents a general model framework for detecting the preferential sampling of environmental monitors recording an environmental process across space and/or time. This is achieved by considering the joint distribution of an environmental process with a site–selection process that considers where and when sites are placed to measure the process. The environmental process may be spatial, temporal or spatio–temporal in nature. By sharing random effects between the two processes, the joint model is able to establish whether site placement was stochastically dependent of the environmental process under study. Furthermore, if stochastic dependence is identified between the two processes, then inferences about the probability distribution of the spatio–temporal process will change, as will predictions made of the process across space and time. The embedding into a spatio–temporal framework also allows for the modelling of the dynamic site—selection process itself. Real–world factors affecting both the size and location of the network can be easily modelled and quantified. Depending upon the choice of population of locations to consider for selection across space and time under the site– selection process, different insights about the precise nature of preferential sampling can be obtained. The general framework developed in the paper is designed to be easily and quickly fit using the R-INLA package. We apply this framework to a case study involving particulate air pollution over the UK where a major reduction in the size of a monitoring network through time occurred. It is demonstrated that a significant response–biased reduction in the air quality monitoring network occurred, namely the relocation of monitoring sites to locations with the highest pollution levels, and the routine removal of sites at locations with the lowest. We also show that the network was consistently unrepresentative of the levels of particulate matter seen across much of GB throughout the operating life of the network. Finally we show that this may have led to a severe over-reporting of the population–average exposure levels experienced across GB. This could have great impacts on estimates of the health effects of black smoke levels.Natural Science and Engineering Research Council of Canad

Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution

This is the author accepted manuscript. Available from arXiv via the URL in this record.Air pollution is a major risk factor for global health, with both ambient and household air pollution contributing substantial components of the overall global disease burden. One of the key drivers of adverse health effects is fine particulate matter ambient pollution (PM2:5) to which an estimated 3 million deaths can be attributed annually. The primary source of information for estimating exposures has been measurements from ground monitoring networks but, although coverage is increasing, there remain regions in which monitoring is limited. Ground monitoring data therefore needs to be supplemented with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. A hierarchical modelling approach for integrating data from multiple sources is proposed allowing spatially-varying relationships between ground measurements and other factors that estimate air quality. Set within a Bayesian framework, the resulting Data Integration Model for Air Quality (DIMAQ) is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world. Bayesian analysis on this scale can be computationally challenging and here approximate Bayesian inference is performed using Integrated Nested Laplace Approximations. Model selection and assessment is performed by cross-validation with the final model offering substantial increases in predictive accuracy, particularly in regions where there is sparse ground monitoring, when compared to previous approaches: root mean square error (RMSE) reduced from 17.1 to 10.7, and population weighted RMSE from 23.1 to 12.1 gm3. Based on summaries of the posterior distributions for each grid cell, it is estimated that 92% of the world’s population reside in areas exceeding the World Health Organization’s Air Quality Guidelines.Matthew Lloyd Thomas is supported by a scholarship from the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa), under the project EP/L015684/1. Amelia Jobling was supported for this work by WHO contracts APW 201255146 and 201255393

Corediscussion Paper 2000/12

We develop and experiment with new upper bounds for the constrained maximum-entropy sampling problem. Our partition bounds are based on Fischer's inequality. Further new upper bounds combine the use of Fischer's inequality with previously developed bounds. We demonstrate this in detail by using the partitioning idea to strengthen the spectral bounds of Ko, Lee and Queyranne and of Lee. Computational evidence suggests that these bounds may be useful in solving problems to optimality in a branch-and-bound framework. Keywords: experimental design, design of experiments, entropy, maximum -entropy sampling, spectral bound, Lagrangian, Fischer's inequality, branch-and-bound, matching, set partitioning. 1 Departmento f Mathematical Sciences, T.J. Watso Research Center, IBM, USA. 2 Departmento f Mathematics, Universityo f Kentucky, USA and CORE, Universite CathoK@b deLob ain, Belgium. E-mail: [email protected]. WWW:http://www.ms.uky.edu/# jlee. 3 Departmento f Mathematics, Earlham ColhamK USA. This paper presents research resultso the Belgian Proiano Interuniversity Pofi o Attractio initiated by the Belgian State, Prime Minister's O#ce, Science Pofififi Pro gramming. The scientific respospK'fi y is assumed by theautho

Assessing the impact of race, social factors and air pollution on birth outcomes: a population-based study

Abstract Background Both air pollution exposure and socioeconomic status (SES) are important indicators of children’s health. Using highly resolved modeled predictive surfaces, we examine the joint effects of air pollution exposure and measures of SES in a population level analysis of pregnancy outcomes in North Carolina (NC). Methods Daily measurements of particulate matter <2.5 μm in aerodynamic diameter (PM2.5) and ozone (O3) were calculated through a spatial hierarchical Bayesian model which produces census-tract level point predictions. Using multilevel models and NC birth data from 2002–2006, we examine the association between pregnancy averaged PM2.5 and O3, individual and area-based SES indicators, and birth outcomes. Results Maternal race and education, and neighborhood household income were associated with adverse birth outcomes. Predicted concentrations of PM2.5 and O3 were also associated with an additional effect on reductions in birth weight and increased risks of being born low birth weight and small for gestational age. Conclusions This paper builds on and complements previous work on the relationship between pregnancy outcomes and air pollution exposure by using 1) highly resolved air pollution exposure data; 2) a five-year population level sample of pregnancies; and 3) including personal and areal level measures of social determinants of pregnancy outcomes. Results show a stable and negative association between air pollution exposure and adverse birth outcomes. Additionally, the more socially disadvantaged populations are at a greater risk; controlling for both SES and environmental stressors provides a better understanding of the contributing factors to poor children’s health outcomes.http://deepblue.lib.umich.edu/bitstream/2027.42/109504/1/12940_2013_Article_720.pd

Decision theoretic estimation using record statistics

Scale parameter, Exponential distribution, Risk reduction, Equivariant estimator, Improved estimation, Mean squared error, Entropy loss, Record statistics,