Dataset Information

Impact of increasing heat waves on U.S. ozone episodes in the 2050s: Results from a multimodel analysis using extreme value theory.

ABSTRACT: We develop a statistical model using extreme value theory to estimate the 2000-2050 changes in ozone episodes across the United States. We model the relationships between daily maximum temperature (Tmax) and maximum daily 8-hour average (MDA8) ozone in May-September over 2003-2012 using a Point Process (PP) model. At ~20% of the sites, a marked decrease in the ozone-temperature slope occurs at high temperatures, defined as ozone suppression. The PP model sometimes fails to capture ozone-Tmax relationships, and so we refit the ozone-Tmax slope using logistic regression and a Generalized Pareto Distribution model. We then apply the resulting hybrid-EVT model to projections of Tmax from an ensemble of downscaled climate models. Assuming constant anthropogenic emissions at the present level, we find an average increase of 2.3 days a-1 in ozone episodes (> 75 ppbv) across the United States by the 2050s, with a change of +3-9 days a-1 at many sites.

SUBMITTER: Shen L

PROVIDER: S-EPMC4930155 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Impact of increasing heat waves on U.S. ozone episodes in the 2050s: Results from a multimodel analysis using extreme value theory.

Shen L L Mickley L J LJ Gilleland E E

Geophysical research letters 20160425 8

We develop a statistical model using extreme value theory to estimate the 2000-2050 changes in ozone episodes across the United States. We model the relationships between daily maximum temperature (<i>Tmax</i>) and maximum daily 8-hour average (MDA8) ozone in May-September over 2003-2012 using a Point Process (PP) model. At ~20% of the sites, a marked decrease in the ozone-temperature slope occurs at high temperatures, defined as ozone suppression. The PP model sometimes fails to capture ozone-< ...[more]

PMID: 27378820

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Project description:The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of reliable associations by the overreduction of statistical power. In this report, we examine 2 alternative approaches to improve the statistical power of a whole genome association study to detect reliable genetic associations. The approaches were tested using the Genetic Analysis Workshop 19 (GAW19) whole genome sequencing data. The first tested method estimates the real number of effective independent tests actually being performed in whole genome association project by the use of an extreme value distribution and a set of phenotype simulations. Given the familiar nature of the GAW19 data and the finite number of pedigree founders in the sample, the number of correlations between genotypes is greater than in a set of unrelated samples. Using our procedure, we estimate that the effective number represents only 15 % of the total number of independent tests performed. However, even using this corrected significance threshold, no genome-wide significant association could be detected for systolic and diastolic blood pressure traits. The second approach implements a biological relevance-driven hypothesis tested by exploiting prior computational predictions on the effect of nonsynonymous genetic variants detected in a whole genome sequencing association study. This guided testing approach was able to identify 2 promising single-nucleotide polymorphisms (SNPs), 1 for each trait, targeting biologically relevant genes that could help shed light on the genesis of the human hypertension. The first gene, PFH14, associated with systolic blood pressure, interacts directly with genes involved in calcium-channel formation and the second gene, MAP4, encodes a microtubule-associated protein and had already been detected by previous genome-wide association study experiments conducted in an Asian population. Our results highlight the necessity of the development of alternative approached to improve the efficiency on the detection of reasonable candidate associations in whole genome sequencing studies.

Dataset Information

Impact of increasing heat waves on U.S. ozone episodes in the 2050s: Results from a multimodel analysis using extreme value theory.

Publications

Impact of increasing heat waves on U.S. ozone episodes in the 2050s: Results from a multimodel analysis using extreme value theory.

Similar Datasets

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