Dataset Information

Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

ABSTRACT: States in the USA are required to demonstrate future compliance of criteria air pollutant standards by using both air quality monitors and model outputs. In the case of ozone, the demonstration tests aim at relying heavily on measured values, due to their perceived objectivity and enforceable quality. Weight given to numerical models is diminished by integrating them in the calculations only in a relative sense. For unmonitored locations, the EPA has suggested the use of a spatial interpolation technique to assign current values. We demonstrate that this approach may lead to erroneous assignments of nonattainment and may make it difficult for States to establish future compliance. We propose a method that combines different sources of information to map air pollution, using the Bayesian Maximum Entropy (BME) Framework. The approach gives precedence to measured values and integrates modeled data as a function of model performance. We demonstrate this approach in North Carolina, using the State's ozone monitoring network in combination with outputs from the Multiscale Air Quality Simulation Platform (MAQSIP) modeling system. We show that the BME data integration approach, compared to a spatial interpolation of measured data, improves the accuracy and the precision of ozone estimations across the state.

SUBMITTER: de Nazelle A

PROVIDER: S-EPMC2912419 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

de Nazelle Audrey A Arunachalam Saravanan S Serre Marc L ML

Environmental science & technology 20100801 15

States in the USA are required to demonstrate future compliance of criteria air pollutant standards by using both air quality monitors and model outputs. In the case of ozone, the demonstration tests aim at relying heavily on measured values, due to their perceived objectivity and enforceable quality. Weight given to numerical models is diminished by integrating them in the calculations only in a relative sense. For unmonitored locations, the EPA has suggested the use of a spatial interpolation ...[more]

PMID: 20590110

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Project description:BackgroundAfrican trypanosomiasis is a tsetse-borne parasitic infection that affects humans, wildlife, and domesticated animals. Tsetse flies are endemic to much of Sub-Saharan Africa and a spatial and temporal understanding of tsetse habitat can aid surveillance and support disease risk management. Problematically, current fine spatial resolution remote sensing data are delivered with a temporal lag and are relatively coarse temporal resolution (e.g., 16 days), which results in disease control models often targeting incorrect places. The goal of this study was to devise a heuristic for identifying tsetse habitat (at a fine spatial resolution) into the future and in the temporal gaps where remote sensing and proximal data fail to supply information.MethodsThis paper introduces a generalizable and scalable open-access version of the tsetse ecological distribution (TED) model used to predict tsetse distributions across space and time, and contributes a geospatial Bayesian Maximum Entropy (BME) prediction model trained by TED output data to forecast where, herein the Morsitans group of tsetse, persist in Kenya, a method that mitigates the temporal lag problem. This model facilitates identification of tsetse habitat and provides critical information to control tsetse, mitigate the impact of trypanosomiasis on vulnerable human and animal populations, and guide disease minimization in places with ephemeral tsetse. Moreover, this BME analysis is one of the first to utilize cluster and parallel computing along with a Monte Carlo analysis to optimize BME computations. This allows for the analysis of an exceptionally large dataset (over 2 billion data points) at a finer resolution and larger spatiotemporal scale than what had previously been possible.ResultsUnder the most conservative assessment for Kenya, the BME kriging analysis showed an overall prediction accuracy of 74.8% (limited to the maximum suitability extent). In predicting tsetse distribution outcomes for the entire country the BME kriging analysis was 97% accurate in its forecasts.ConclusionsThis work offers a solution to the persistent temporal data gap in accurate and spatially precise rainfall predictions and the delayed processing of remotely sensed data collectively in the - 45 days past to + 180 days future temporal window. As is shown here, the BME model is a reliable alternative for forecasting future tsetse distributions to allow preplanning for tsetse control. Furthermore, this model provides guidance on disease control that would otherwise not be available. These 'big data' BME methods are particularly useful for large domain studies. Considering that past BME studies required reduction of the spatiotemporal grid to facilitate analysis. Both the GEE-TED and the BME libraries have been made open source to enable reproducibility and offer continual updates into the future as new remotely sensed data become available.

Project description:Previous research shows that educational attainment is a protective factor for substance use and sexual risk among adolescents and young adults. Evidence also shows that this relationship may differ by race/ethnicity and gender. This study aimed to elucidate the relationship between educational attainment, substance use and sexual risk among African American women in emerging adulthood. This study uses cross-sectional data from 646 African American women (aged 18 to 25) enrolled in a randomized trial of a behavioral HIV risk-reduction intervention. At enrollment, participants completed a risk behavior assessment via audio-computer assisted self-interview and provided a urine sample for drug screening. Bivariate logistic regression analyses were conducted to examine substance use and sexual risk factors associated with educational attainment: completing some college or more vs. completing high school or less). Participants who completed some college or more (52%) were more likely to report heavy alcohol use (four or more drinks in one day) in the past 30 days (OR=1.48; p=0.014) and more likely to report alcohol or other drug use just before or during last sex (OR=1.43; p=0.026) compared with participants who completed high school or less. Completing some college or more was protective for having a positive urine screen for cocaine (OR=0.43; p=0.018) and reporting condomless sex at last sex (OR=0.71; p=0.041). Differences in positive marijuana screens, reporting a previous STI, or reporting their partner used alcohol or other drugs at last sex were not statistically significant. The findings reveal notable differences in the magnitude and direction of associations between educational attainment and substance use and sexual risk. Although educational attainment is subject to change because of the frequent pursuit of education during emerging adulthood, the findings may have important implications for tailoring HIV risk-reduction interventions to key populations, such as African American women.

Dataset Information

Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

Publications

Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

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