October 9, 2008 Validation of a dispersion model specific for odours by comparison with the observations of panellists acc...
<ul><li>Why is it so important that a dispersion model reflects reality? </li></ul><ul><li>To objectively define the odour...
2. Proposed model specific for odours: Gauss-Gifford model <ul><li>Gifford:  </li></ul><ul><ul><li>Considers the probabili...
<ul><li>3.1 Types of validation methods </li></ul><ul><li>Comparison with field measurements </li></ul><ul><li>Comparison ...
3.2 Use of statistical indicators for model performance evaluation 3. Model validation method
<ul><li>4.1 Chosen activity: Residues incineration activity (TERSA) </li></ul><ul><li>Located in a very sensitive odour ar...
<ul><li>4.2 First calculation method: Immission study (VDI 3940) </li></ul><ul><li>Grid of 30 equidistant points separated...
<ul><li>4.3 Second calculation method: Impact modelling (Gauss-Gifford model) </li></ul><ul><li>Odour sources: Quantified ...
<ul><li>4.4 Comparison limitations intrinsic to the methodologies used </li></ul><ul><li>Meteorological data: 5 years for ...
<ul><li>5.1 Odour impact maps </li></ul><ul><li>Odour perception threshold exceeding time: 32% (obs) / 33% (model). </li><...
<ul><li>5.2 Individual point evaluation </li></ul><ul><li>Figures show the ratio modelled odour frequency / observed odour...
<ul><li>5.3 Performance indicators </li></ul><ul><li>Factor 2: More than 44% of the values. </li></ul><ul><li>Factor 5: 84...
<ul><li>Comparison results are considered excellent considering the intrinsic comparison limitations of both methods (mode...
October 9, 2008 Validation of a dispersion model specific for odours by comparison with the observations of panellists acc...
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Rosa Arias 3rd Iwa Odour Congress

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Validation of a dispersion model specific for odours by comparison
with the observations of panellists according to the German standard
VDI 3940

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Rosa Arias 3rd Iwa Odour Congress

  1. 1. October 9, 2008 Validation of a dispersion model specific for odours by comparison with the observations of panellists according to the German standard VDI 3940 R. Arias*, P. Barnéoud**, M.A. Piró* and F. Recasens* * Strengths (e-mail: [email_address] , [email_address] ) ** Odotech (e-mail: [email_address] )
  2. 2. <ul><li>Why is it so important that a dispersion model reflects reality? </li></ul><ul><li>To objectively define the odour problem </li></ul><ul><li>To eliminate odour complaints </li></ul><ul><li>To control the odour impact generated by an emitting activity </li></ul><ul><ul><li>Establishment of corrective measurements </li></ul></ul><ul><ul><li>Chimneys dimensioning </li></ul></ul><ul><li>Tool for Environmental Authorities => Regulations </li></ul>1. Characteristics of a dispersion model How do we guarantee that the dispersion model reflects reality? <ul><li>Specific model for odours: It should consider the human perception of odours (concentration fluctuations rather than average values) </li></ul><ul><li>It should be validated </li></ul>
  3. 3. 2. Proposed model specific for odours: Gauss-Gifford model <ul><li>Gifford: </li></ul><ul><ul><li>Considers the probability of appearance of odour peaks near the emission sources, which cause the complaints </li></ul></ul><ul><ul><li>Its contribution disappears far from the emitting sources (homogeneous plume) </li></ul></ul><ul><ul><li>Specific for odours: It estimates the concentration fluctuations. </li></ul></ul><ul><li>Gauss-Gifford model: The estimated concentration peaks are added to the average Gaussian concentrations to better represent the dispersion of odours. </li></ul>
  4. 4. <ul><li>3.1 Types of validation methods </li></ul><ul><li>Comparison with field measurements </li></ul><ul><li>Comparison with wind tunnels measurements </li></ul><ul><li>Comparison with the results of an advanced dispersion model </li></ul>3. Model validation method <ul><li>3 methods used: </li></ul><ul><li>Wind tunnel experiments </li></ul><ul><li>Comparison with field observations in Montreal </li></ul><ul><li>Comparison with field observations in Barcelona </li></ul>Validation method developed
  5. 5. 3.2 Use of statistical indicators for model performance evaluation 3. Model validation method
  6. 6. <ul><li>4.1 Chosen activity: Residues incineration activity (TERSA) </li></ul><ul><li>Located in a very sensitive odour area in Barcelona (Forum) </li></ul><ul><li>Surrounded by other odour emitting activities (Ecoparc, WWTS, etc.) </li></ul>4. Case Study for the model validation
  7. 7. <ul><li>4.2 First calculation method: Immission study (VDI 3940) </li></ul><ul><li>Grid of 30 equidistant points separated 100m </li></ul><ul><li>Panellists trained on field to identify odours coming from Tersa & other activities </li></ul><ul><li>Panellists calibrated according to EN 13725 </li></ul><ul><li>Individual measurements every 3 days during 12 weeks (28 measurements) </li></ul>4. Case Study for the model validation 20 1
  8. 8. <ul><li>4.3 Second calculation method: Impact modelling (Gauss-Gifford model) </li></ul><ul><li>Odour sources: Quantified by dynamic olfactometry </li></ul><ul><ul><li>Exhaust gas chimney. </li></ul></ul><ul><ul><li>Discharge platform (dirty with leachate during discharge hours) </li></ul></ul><ul><ul><li>Discharge pit (permanent open doors during night and day) </li></ul></ul><ul><ul><li>Access road (truck contribution) </li></ul></ul><ul><li>Hourly meteorological Data (5 years) </li></ul><ul><li>Receptor grid: </li></ul><ul><li>81 points separated 50m (400m x 400m) </li></ul>4. Case Study for the model validation
  9. 9. <ul><li>4.4 Comparison limitations intrinsic to the methodologies used </li></ul><ul><li>Meteorological data: 5 years for the impact study (hourly data). </li></ul><ul><li>Measurement period: Only 3 months for the immission study. </li></ul><ul><li>Measurement discontinuity: 28 punctual measurements for the immission study at 30 individual grid points (random timetable). </li></ul><ul><li>Hypothesis for odour sources characterisation: Discharge pit (building ventilation models => added uncertainty), trucks contribution assumptions. </li></ul><ul><li>Topography or building effects: Not considered by the dispersion model. </li></ul><ul><li>Human measurement uncertainty: Measurements based on the recognition threshold => Odour type attribution mistakes? </li></ul>4. Case Study for the model validation
  10. 10. <ul><li>5.1 Odour impact maps </li></ul><ul><li>Odour perception threshold exceeding time: 32% (obs) / 33% (model). </li></ul><ul><li>Both results showed a quick odour concentration decreasing with distance. </li></ul><ul><li>100m circle: 12% odour perception frequencies (obs) / 6% (model). </li></ul><ul><li>SW region (max impact): Same distance 4% (obs) / 3% (model). </li></ul>5. Comparison and validation results
  11. 11. <ul><li>5.2 Individual point evaluation </li></ul><ul><li>Figures show the ratio modelled odour frequency / observed odour frequency versus the observed odour frequency or the distance from the emitters. </li></ul><ul><li>High dispersion expectable considering the intrinsic comparison limitations. </li></ul>5. Comparison and validation results
  12. 12. <ul><li>5.3 Performance indicators </li></ul><ul><li>Factor 2: More than 44% of the values. </li></ul><ul><li>Factor 5: 84% of the values. </li></ul><ul><li>Mean performance indicators for 2 advanced dispersion models (Aermod and Adms), compared with five observation series. </li></ul><ul><li>Worst result: Geometric Variance, due to the intrinsic comparison limitations. </li></ul>5. Comparison and validation results
  13. 13. <ul><li>Comparison results are considered excellent considering the intrinsic comparison limitations of both methods (modelling and observations). </li></ul><ul><li>Both methods produced a maximum odour threshold percentage exceeding time almost identical (32% for the observations and 33% for the modelling). </li></ul><ul><li>More than 44% of the calculated values lay within a factor of 2 with regard to the observations. </li></ul><ul><li>The statistics parameters show that the model represents well the atmospheric dispersion of odours no matter the distance to the emitters. </li></ul><ul><li>The performance of the Gauss-Gifford model is considered of the same quality as the Aermod model. </li></ul>6. Conclusions The dispersion model applied provides a good representation of the real odour impact Powerful tool to control odour nuisance & for regulation
  14. 14. October 9, 2008 Validation of a dispersion model specific for odours by comparison with the observations of panellists according to the German standard VDI 3940 R. Arias*, P. Barnéoud**, M.A. Piró* and F. Recasens* * Strengths (e-mail: [email_address] , [email_address] ) ** Odotech (e-mail: [email_address] ) Thank you

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