Global and local sensitivity analysis of the Emission Dispersion Model input parameters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samia Chettouh
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Dispersion Modeling ◽  
Content Type ◽  
Initial Concentration ◽  
Model Input ◽  
Input Parameters ◽  
Industrial Fire

PurposeThe objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria.Design/methodology/approachThe SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.FindingsThe study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation.Originality/valueThe study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

scholarly journals Sensitivity analysis of the meteorological preprocessor MPP-FMI 3.0 using algorithmic differentiation

2017 ◽  
Vol 10 (10) ◽  
pp. 3793-3803 ◽  
Author(s):  
John Backman ◽  
Curtis R. Wood ◽  
Mikko Auvinen ◽  
Leena Kangas ◽  
Hanna Hannuniemi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Friction Velocity ◽  
Meteorological Data ◽  
Solar Irradiation ◽  
Obukhov Length ◽  
Algorithmic Differentiation ◽  
Model Input ◽  
Input Parameters ◽  
Meteorological Input

Abstract. The meteorological input parameters for urban- and local-scale dispersion models can be evaluated by preprocessing meteorological observations, using a boundary-layer parameterisation model. This study presents a sensitivity analysis of a meteorological preprocessor model (MPP-FMI) that utilises readily available meteorological data as input. The sensitivity of the preprocessor to meteorological input was analysed using algorithmic differentiation (AD). The AD tool used was TAPENADE. The AD method numerically evaluates the partial derivatives of functions that are implemented in a computer program. In this study, we focus on the evaluation of vertical fluxes in the atmosphere and in particular on the sensitivity of the predicted inverse Obukhov length and friction velocity on the model input parameters. The study shows that the estimated inverse Obukhov length and friction velocity are most sensitive to wind speed and second most sensitive to solar irradiation. The dependency on wind speed is most pronounced at low wind speeds. The presented results have implications for improving the meteorological preprocessing models. AD is shown to be an efficient tool for studying the ranges of sensitivities of the predicted parameters on the model input values quantitatively. A wider use of such advanced sensitivity analysis methods could potentially be very useful in analysing and improving the models used in atmospheric sciences.

scholarly journals Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 using algorithmic differentiation

2017 ◽  
Author(s):  
John Backman ◽  
Curtis Wood ◽  
Mikko Auvinen ◽  
Leena Kangas ◽  
Hanna Hannuniemi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Friction Velocity ◽  
Meteorological Data ◽  
Solar Irradiation ◽  
Obukhov Length ◽  
Algorithmic Differentiation ◽  
Model Input ◽  
Input Parameters ◽  
Meteorological Input

Abstract. The meteorological input parameters for urban and local scale dispersion models can be evaluated by pre-processing meteorological observations, using a boundary-layer parametrization model. This study presents a sensitivity analysis of a meteorological pre-processor model (MPP-FMI) that utilises readily available meteorological data as input. The sensitivity of the pre-processor to meteorological input was analysed using algorithmic differentiation (AD). The AD tool used was TAPENADE. The AD method numerically evaluates the partial derivatives of functions that are implemented in a computer program. In this study, we focus on the evaluation of vertical fluxes in the atmosphere, and in particular on the sensitivity of the predicted inverse Obukhov length and friction velocity on the model input parameters. The study shows that the estimated inverse Obukhov length and friction velocity are most sensitive to wind speed, and second most sensitive to solar irradiation. The dependency on wind speed is most pronounced at low wind speeds. The presented results have implications for improving the meteorological pre-processing models. AD is shown to be an efficient tool for studying the ranges of sensitivities of the predicted parameters on the model input values quantitatively. A wider use of such advanced sensitivity analysis methods could potentially be very useful in analysing and improving the models used in atmospheric sciences.

An Evaluation of Two NEXRAD Wind Retrieval Methodologies and Their Use in Atmospheric Dispersion Models

2008 ◽  
Vol 47 (9) ◽  
pp. 2351-2371 ◽  
Author(s):  
Jerome D. Fast ◽  
Rob K. Newsom ◽  
K. Jerry Allwine ◽  
Qin Xu ◽  
Pengfei Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Temporal Variations ◽  
Statistical Technique ◽  
Wind Fields ◽  
Model Framework ◽  
Background Error ◽  
Wind Retrievals ◽  
Simpler Method

Abstract Two entirely different methods for retrieving 3D fields of horizontal winds from Next Generation Weather Radar (NEXRAD) radial velocities have been evaluated using radar wind profiler measurements to determine whether routine wind retrievals would be useful for atmospheric dispersion model applications. The first method uses a physical algorithm based on four-dimensional variational data assimilation, and the second simpler method uses a statistical technique based on an analytic formulation of the background error covariance. Both methods can be run in near–real time, but the simpler method was executed about 2.5 times as fast as the four-dimensional variational method. The observed multiday and diurnal variations in wind speed and direction were reproduced by both methods below ∼1.5 km above the ground in the vicinity of Oklahoma City, Oklahoma, during July 2003. However, wind retrievals overestimated the strength of the nighttime low-level jet by as much as 65%. The wind speeds and directions obtained from both methods were usually similar when compared with profiler measurements, and neither method outperformed the other statistically. Within a dispersion model framework, the 3D wind fields and transport patterns were often better represented when the wind retrievals were included along with operational data. Despite uncertainties in the wind speed and direction obtained from the wind retrievals that are higher than those from remote sensing radar wind profilers, the inclusion of the wind retrievals is likely to produce more realistic temporal variations in the winds aloft than would be obtained by interpolation using the available radiosondes, especially during rapidly changing synoptic- and mesoscale conditions.

scholarly journals GEOSTATISTICS FOR AIR QUALITY MAPPING: CASE OF BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 353-359
Author(s):  
R. V. Ramos ◽  
A. C. Blanco
Keyword(s):  
Air Quality ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Spatial Variations ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Night Time ◽  
Smoothing Factor ◽  
Pollutant Concentrations ◽  
Atmospheric Dispersion Models

Abstract. Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.

The job rotation scheduling problem considering human cognitive effects: an integrated approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashkan Ayough ◽  
Farbod Farhadi ◽  
Mostafa Zandieh
Keyword(s):  
Sensitivity Analysis ◽  
Optimization Model ◽  
Design Methodology ◽  
Heuristic Algorithms ◽  
Job Rotation ◽  
Content Type ◽  
Input Parameters ◽  
Job Rotation Scheduling ◽  
The Impact ◽  
First Time

Purpose This paper aims to unfold the role that job rotation plays in a lean cell. Unlike many studies, the authors consider heterogeneous operators with dynamic performance factor that is impacted by the assignment and scheduling decisions. The purpose is to derive an understanding of the underlying effects of job rotations on performance metrics in a lean cell. The authors use an optimization framework and an experimental design methodology for sensitivity analysis of the input parameters. Design/methodology/approach The approach is an integration of three stages. The authors propose a set-based optimization model that considers human behavior parameters. They also solve the problem with two meta-heuristic algorithms and an efficient local search algorithm. Further, the authors run a post-optimality analysis by conducting a design of experiments using the response surface methodology (RSM). Findings The results of the optimization model reveal that the job rotation schedules and the human cognitive metrics influence the performance of the lean cell. The results of the sensitivity analysis further show that the objective function and the job rotation frequencies are highly sensitive to the other input parameters. Based on the findings from the RSM, the authors derive general rules for the job rotations in a lean cell given the ranges in other input variables. Originality/value The authors integrate the job rotation scheduling model with human behavioral and cognitive parameters and formulate the problem in a lean cell for the first time in the literature. In addition, they use the RSM for the first time in this context and offer a post-optimality analysis that reveals important information about the impact of the job rotations on the performance of operators and the entire working cell.

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