John bruce

Modelling dust emissions

By John Bruce

1

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

2

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

3

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

4

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

5

Site one overview

6

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

7

‘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

8

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

9

Weather-dust trends

• Temperatures positively correlated with dust levels

• Rainfall negatively correlated with dust levels

• Wind influence dependant on specific directional winds

10

Weather-dust trends

• Temperatures positively correlated with dust levels

• Rainfall negatively correlated with dust levels

• Wind influence dependant on specific directional winds

11

Model finalisation

• Incorporates all relevant weather parameters

• Representative of dust patterns

• Custom adjustments for each site

12

Site one model

13

Site two model

14

Model evaluation: site one

• One year of original data (r2 = 0.67)

• 6 months of supplementary data (r2 = 0.76)

15


16


17


18


19

Model evaluation: site two

• Twelve months of original data (r2 = 0.67)

• Six months of supplementary data (r2 = 0.45)

20


21


22


23


24

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

25






ADMS trials


26






ADMS trials


27

Conclusions

• Low cost, modest dust monitoring

• Trends visible for individual sites

• Simple but effective baseline modelling

29

Thank you for your attention

• Any questions?

• Contact: [email protected]

29

John bruce

Environment

Transcript of John bruce