Sentence examples for model for pollutant from inspiring English sources

An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function.

For an unstudied reach it is possible to find a model for pollutant transport based on the comparison between the characteristics of that river reach with the characteristics of the already studied reaches, stored in the case base.

The study confirmed the validity of the use of mathematical techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for source identification and principal component analysis/absolute principal component scores (PCA/APCS) receptor model for pollutant load quantification.

Methylene blue (MB), methyl orange (MO), and phenol red (PR) are representing a thiazine, an azo, and a tryarilmethane dye respectively, which was spiked with kaolinite were selected as a model for pollutant dyes tests.

In the current study, authors previous model (multiple cell model for pollutants dispersion from a network of industrial stacks) was completed.

The developed tool is also applicable in the case of other rivers were no detailed concentration measurements are available and the mathematical models for pollutant transport are needed.

Regression modeling including the significant associations (i.e., those lags marked with *) discussed above (i.e., all pollutants but SO2), and controlling for confounding variables, was used to identify the final multi-pollutant model for the pollutants ozone and NO2.

Further, we performed two-pollutant models for pollutants that were significantly (p < 0.05) associated with FEV1 in one-pollutant models.

Our implementation of SPCA proceeded as follows: Fit a separate Poisson log-linear model for each pollutant variable, relating the confounders and the given pollutant to the adverse health outcomes.

To predict outdoor concentration of pollutants at MESA Air participant residences, we fit a separate spatiotemporal exposure prediction model for each pollutant (PM2.5, NO2, NOx, and LAC) in each metropolitan region.

The rather complex MUSIC and simple KAREN models were compared in terms of predicting catchment runoff, while an empirical regression model was compared to a process-based build-up/wash-off model for stormwater pollutant prediction.