Sentence examples for exposure prediction from inspiring English sources

To estimate these associations, epidemiologic studies develop exposure prediction models to predict pollutant concentrations over long periods of time at cohort home addresses based on monitoring data from regulatory networks or study-specific monitoring campaigns.

The NPACT exposure prediction model aimed to predict 2-week average concentrations of PM2.5 components at participant addresses by adopting the spatiotemporal modeling framework developed for the MESA Air study.

Additional research is clearly needed to determine if a similarly predictable relationship exists for other metrics of exposure prediction (such as those proposed for logistic regression ), and whether apparent nonlinearities between the prediction of exposure and confounding amplification at more extreme ranges of prediction can be addressed quantitatively.

In the original plan, the NPACT monitoring data were intended to be combined with regulatory monitoring data in exposure prediction models, similar to the approach used previously for predicting PM2.5 (Keller et al. 2015; Paciorek et al. 2009; Sampson et al. 2011; Yanosky et al. 2009).

The findings have implications for better understanding the behavior of polymer-coated nanoparticles in organic-presented groundwater systems, i.e., detachment-associated uncertainty in exposure prediction of the nanomaterials.

Here we present the design, methods, and results of the Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR) on ambient concentrations of benzene, toluene, ethylbenzene, p-xylene, m-xylene, o-xylene (BTEX), and total BTEX.

In particular, we developed two land use regression models for NO2 in the city of Rome with a time span of 12 years, and compared them in terms of observed and predicted values, exposure predictions, and association with mortality for natural causes in a large cohort of adult residents followed from 2001 to 2006.

In silico pharmacokinetic modules were developed for rapid exposure predictions via diverse exposure routes.

A weak relationship was found between the facility-specific monthly electric power consumption and the GM magnetic field magnitude, but confidence limits were too broad to make meaningful exposure predictions from electric power data.

An important trend of such models is their coupling with an urban model or a computational fluid dynamic model for urban/local scale applications at 1 km or less and with an exposure model to provide real-time public health assessment and exposure predictions.

The Exposure tab contains a series of sub-tabs providing access to the following types of data, when available, for a particular chemical: (1) Product and Use Categories; (2) Chemical Weight Fraction; (3) Functional Use; (4) Monitoring Data; and, (5) Exposure Predictions.