Procedures for estimation of modelling uncertainty in air quality assessment.

The main objectives of this work focus, firstly, on a review of the current existent methodologies to estimate air quality modelling uncertainty, and, secondly, in the preparation of guidelines for modelling uncertainty estimation, which can be used by local and regional authorities responsible for air quality management. From the application exercise, it was concluded that it is possible to define a subset of statistical parameters able to reproduce the general uncertainties estimation. Concerning the quality indicators defined by EU directives, the results show that the legislated uncertainty estimation measures are ambiguous and inadequate in several aspects, mainly in what concerns the error measures for hourly and daily indicators based on the highest observed concentration. A relative error at the percentile correspondent to the allowed number of exceedances of the limit value was suggested and tested, showing that is a more robust and appropriate parameter for model performance evaluation.

[1]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[2]  William R. Stockwell,et al.  First-order sensitivity analysis of models with time-dependent parameters: an application to PAN and ozone , 1999 .

[3]  J. Flemming,et al.  Testing model accuracy measures according to the EU directives : examples using the chemical transport model REM-CALGRID , 2007 .

[4]  R. Errico.  What is an adjoint model , 1997 .

[5]  Jana B. Milford,et al.  Use of sensitivity analysis to compare chemical mechanisms for air-quality modeling , 1992 .

[6]  Robert A Harley,et al.  Adjoint sensitivity analysis for a three-dimensional photochemical model: implementation and method comparison. , 2006, Environmental science & technology.

[7]  R. Sivacoumar,et al.  LINE SOURCE MODEL FOR VEHICULAR POLLUTION PREDICTION NEAR ROADWAYS AND MODEL EVALUATION THROUGH STATISTICAL ANALYSIS , 1999 .

[8]  C Borrego,et al.  Air quality assessment for Portugal. , 2007, The Science of the total environment.

[9]  S. Hanna,et al.  Air quality model performance evaluation , 2004 .

[10]  Laurent Menut,et al.  Adjoint modeling for atmospheric pollution process sensitivity at regional scale , 2003 .

[11]  G. Carmichael,et al.  Direct and adjoint sensitivity analysis of chemical kinetic systems with KPP: Part I—theory and software tools , 2003, Atmospheric Environment.

[12]  Robert Vautard,et al.  A comparison of simulated and observed ozone mixing ratios for the summer of 1998 in Western Europe , 2001 .

[13]  Robert Vautard,et al.  Validation of a hybrid forecasting system for the ozone concentrations over the Paris area , 2001 .

[14]  Richard J. Londergan,et al.  Sampled Monte Carlo uncertainty analysis for photochemical grid models , 2001 .

[15]  Ana Isabel Miranda,et al.  Long-term simulations of photo oxidant pollution over Portugal using the CHIMERE model , 2005 .

[16]  F. Potra,et al.  Sensitivity analysis for atmospheric chemistry models via automatic differentiation , 1997 .

[17]  Armistead G Russell,et al.  High-order, direct sensitivity analysis of multidimensional air quality models. , 2003, Environmental science & technology.

[18]  Luca Delle Monache,et al.  Ozone ensemble forecasts: 1. A new ensemble design , 2006 .

[19]  Ari Karppinen,et al.  A modelling system for predicting urban air pollution:: comparison of model predictions with the data of an urban measurement network in Helsinki , 2000 .

[20]  Carmen J. Nappo,et al.  Modeling dispersion from near-surface tracer releases at Cape Canaveral, Florida , 2001 .

[21]  J. Davies,et al.  Hazardous gas model evaluation with field observations , 1995 .

[22]  Bruno Sportisse,et al.  A sensitivity analysis study for radm2 mechanism using automatic differentiation , 2003 .

[23]  C. Borrego,et al.  Emission and dispersion modelling of Lisbon air quality at local scale , 2003 .

[24]  Matthias Beekmann,et al.  Monte Carlo uncertainty analysis of a regional‐scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign , 2003 .

[25]  Wilson H. Tang,et al.  Decision, risk and reliability , 1984 .

[26]  H. Christopher Frey,et al.  Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain , 2001 .

[27]  Hendrik Elbern,et al.  Ozone episode analysis by four-dimensional variational chemistry data assimilation , 2001 .

[28]  Mehmet T. Odman,et al.  Nonlinearity in atmospheric response: A direct sensitivity analysis approach , 2004 .

[29]  Tolga Elbir,et al.  Comparison of model predictions with the data of an urban air quality monitoring network in Izmir, Turkey , 2003 .

[30]  Wilson H. Tang,et al.  Probability concepts in engineering planning and design , 1984 .

[31]  Yoichi Ichikawa,et al.  An Atmospheric Dispersion Model for the Environmental Impact Assessment of Thermal Power Plants in Japan—A Method for Evaluating Topographical Effects , 2002, Journal of the Air & Waste Management Association.

[32]  Joakim Langner,et al.  Aerosol Modeling : Results and Intercomparison from European Regional-scale Modeling Systems A contribution to the EUROTRAC-2 subproject GLOREAM April 2003 , 2003 .

[33]  R. Pielke.  Mesoscale Meteorological Modeling , 1984 .

[34]  Dongming Hwang,et al.  An automatic differentiation technique for sensitivity analysis of numerical advection schemes in air quality models , 1997 .