John Bruce developed models to predict dust emissions at two industrial sites based on weather data and directional dust monitoring. Linear regression was used to establish correlations between weather parameters like temperature, rainfall, wind speed and direction with measured dust levels. This formed initial models that were able to explain 67-76% of dust levels at the first site and 67-45% at the second site when evaluated. Future work will focus on incorporating site activities and adjusting the models for additional sites.

1. Modelling dust emissions
By John Bruce
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2. Methods for dust prediction
•Knowledge Transfer Partnership
•Investigation into dust modelling
•Based on primary collected data
•Directional sticky-pad dust collection
•Weather data collected at on site meteorological stations
•First approach using two sites
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3. Directional dust monitoring
•Sticky pad dust monitors
Oriented to north
Widely used at UK industrial sites
•Sample array set up, including:
Background locations
On-site monitors
Site boundaries
Progressively closer to and at receptors
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4. Directional dust data
•Directional dust samples sealed using transparent film
•Scanned & analysed for directional:
Dust coverage (AAC%)
Dust soiling (EAC%)
•Further analysis possible:
Gravimetry
Mass spectrometry
Particle size analysis
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5. Site one information & characteristics
•Industrial site undergoing major construction expansion
•500 hectare site
•Near the coast of an inland sea
•Dust is a natural environmental concern
•Predominant winds from north of the site
•Large and high quality data set available
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6. Site one overview
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7. Site two information & characteristics
•Site two chosen to contrast to site one
•10 hectare sand and gravel quarry
•Located in southern England
•Sensitive receptor to the north east
•Considerable existing data
•Flexible dust monitoring available
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8. ‘Background dust’
•‘Background dusts’ defined
•Dusts attributed to natural causes
•From north at site one
•From south-west at site two
•Simplifies initial model
•Anthropogenic dusts added later
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9. Model development
•Parameters at both sites included:
Daily, weekly & weighted rainfall
Temperature
Wind speed, direction & frequency
Measured dust
•Strongest correlations established
•Linear regression used to form an initial model
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10. Weather-dust trends
•Temperatures positively correlated with dust levels
•Rainfall negatively correlated with dust levels
•Wind influence dependant on specific directional winds
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11. Weather-dust trends
•Temperatures positively correlated with dust levels
•Rainfall negatively correlated with dust levels
•Wind influence dependant on specific directional winds
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12. Model finalisation
•Incorporates all relevant weather parameters
•Representative of dust patterns
•Custom adjustments for each site
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13. Site one model
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14. Site two model
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15. Model evaluation: site one
•One year of original data (r2= 0.67)
•6 months of supplementary data (r2 = 0.76)
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16. Model evaluation: site one
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17. Model evaluation: site one
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18. Model evaluation: site one
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19. Model evaluation: site one
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20. Model evaluation: site two
•Twelve months of original data (r2= 0.67)
•Six months of supplementary data (r2 = 0.45)
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21. Model evaluation: site two
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22. Model evaluation: site two
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23. Model evaluation: site two
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24. Model evaluation: site two
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25. Future developments and other projects
•Adding in site workings
•Adjustment for further sites
•Other projects include:
Emission factors -measuring dust take off
ADMS trials
Sticky pad efficiency calculations
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26. Future developments and other projects
•Adding in site workings
•Adjustment for further sites
•Other projects include:
Emission factors -measuring dust take off
ADMS trials
Sticky pad efficiency calculations
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27. Future developments and other projects
•Adding in site workings
•Adjustment for further sites
•Other projects include:
Emission factors -measuring dust take off
ADMS trials
Sticky pad efficiency calculations
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28. Conclusions
•Low cost, modest dust monitoring
•Trends visible for individual sites
•Simple but effective baseline modelling
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29. Thank you for your attention
•Any questions?
•Contact:john.bruce@port.ac.uk
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