A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model

Thor Bjørn Ottosen, Matthias Ketzel, Henrik Skov, Ole Hertel, Jørgen Brandt, Konstantinos Kakosimos

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPM®). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to successfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.

Original languageEnglish
Pages (from-to)165-176
Number of pages12
JournalEnvironmental Modelling and Software
Volume84
DOIs
Publication statusPublished - 1 Oct 2016

Fingerprint

street canyon
Air pollution
Parameter estimation
atmospheric pollution
methodology
outlier
Sensitivity analysis
sensitivity analysis
analysis
parameter estimation
Pollution
Mathematical models
pollution
parameter
Uncertainty
prediction

Keywords

  • Data splitting
  • Exploratory data analysis
  • Matlab
  • OSPM
  • Sensitivity
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modelling

Cite this

A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model. / Ottosen, Thor Bjørn; Ketzel, Matthias; Skov, Henrik; Hertel, Ole; Brandt, Jørgen; Kakosimos, Konstantinos.

In: Environmental Modelling and Software, Vol. 84, 01.10.2016, p. 165-176.

Research output: Contribution to journalArticle

Ottosen, Thor Bjørn ; Ketzel, Matthias ; Skov, Henrik ; Hertel, Ole ; Brandt, Jørgen ; Kakosimos, Konstantinos. / A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model. In: Environmental Modelling and Software. 2016 ; Vol. 84. pp. 165-176.
@article{4c9525d6343e42cf9a6e1880408d38d0,
title = "A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model",
abstract = "Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPM{\circledR}). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to successfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.",
keywords = "Data splitting, Exploratory data analysis, Matlab, OSPM, Sensitivity, Uncertainty",
author = "Ottosen, {Thor Bj{\o}rn} and Matthias Ketzel and Henrik Skov and Ole Hertel and J{\o}rgen Brandt and Konstantinos Kakosimos",
year = "2016",
month = "10",
day = "1",
doi = "10.1016/j.envsoft.2016.06.022",
language = "English",
volume = "84",
pages = "165--176",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model

AU - Ottosen, Thor Bjørn

AU - Ketzel, Matthias

AU - Skov, Henrik

AU - Hertel, Ole

AU - Brandt, Jørgen

AU - Kakosimos, Konstantinos

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPM®). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to successfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.

AB - Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPM®). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to successfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.

KW - Data splitting

KW - Exploratory data analysis

KW - Matlab

KW - OSPM

KW - Sensitivity

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=84978645323&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978645323&partnerID=8YFLogxK

U2 - 10.1016/j.envsoft.2016.06.022

DO - 10.1016/j.envsoft.2016.06.022

M3 - Article

AN - SCOPUS:84978645323

VL - 84

SP - 165

EP - 176

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

ER -