Sentence examples for air quality modeling from inspiring English sources

The objective of this work is to present a novel approach to design a source receptor relationship for air quality modeling.

Increased focus has been directed at fine-scale modeling for improving the ability of air quality modeling systems to capture local phenomena.

Meteorological conditions such as thermal inversions can prevent surface air from rising, trapping pollutants near the surface, which makes accurate forecasts of such events crucial for air quality modeling.

We also elected to use air monitor data to avoid the uncertainty associated with air quality modeling assumptions such as emissions inventories and chemical mixing (U.S. EPA 2008b).

The first step is sometimes accomplished through emissions scenarios and information regarding how emissions translate into pollutant concentrations, such as with air quality modeling systems.

For example, a recent study [ 13] applied a sophisticated air quality modeling system to estimate O3 levels across different regions of the world, whereas other approaches [ 12, 42] applied uniform reductions assuming equal percent reductions in pollutants across all areas.

A new dynamic adaptive grid algorithm has been developed for use in air-quality modeling.

Performance achieved in model problem simulations indicates that the algorithm has the potential to provide accurate air-quality modeling solutions at costs that may be significantly less than those incurred in obtaining equivalent static grid solutions.

Dynamical models, although still imperfect, have improved in recent years and are now widely accepted for many air-quality modeling applications.

This is shown clearly in the results of an analysis of variance, which decomposes the total variability in estimated mortality into the variability due to the chosen climate change air quality modeling system, population projection, epidemiological study (C R function) used, and interactions between these modeling choices, respectively (see Table 4).

Climate change air quality modeling systems.