This document describes a new low-cost method for particulate matter source apportionment using particle number size distribution analysis. It has been tested on two sites: a construction site for the HS2 rail project in Birmingham, and a granite quarry in Leicestershire. The method was able to identify unique particle profiles associated with different pollution sources at each site, such as construction activities or quarry works. It shows potential to automate source apportionment using machine learning and a growing database of particle profiles from known sources. Future work includes applying this technique to establish better emission factors and expand its use in environmental impact assessments.

1. Low cost source apportionment for
particulate matter
Oct 2023

2. The problem & published paper
• Particulate Matter source apportionment has been too expensive to be an ‘available
technique’ until now, requiring a >£100k worth of equipment and expert analysis
• Understanding PM sources is key to their management
• We are working towards making source apportionment techniques ‘available’ to this AQ
community

3. Methodology
• Particulate matter can be used as a marker
for different sources of pollution as each
source presents a unique particle number
size distribution
• Statistical methods are used to separate the
particle profiles, and using additional
information (meteorological conditions,
temporal variations etc.) they are assigned
to their corresponding sources.

4. HS2 Curzon Street
• The future HS2 terminus station is in the
city centre of Birmingham, currently a
construction site.
• Being within the urban area of Birmingham
the site is also affected by other sources of
pollution
• During the measuring period little variation
was found for the meteorological conditions.

5. Analysis results
• The effect of the construction site was
pinpointed by two separate particle profiles
(factors 3 and 4).
• Additionally, one more strong source of fine
particles is identified in the southeast, being
associated with activities at the train station
on that side (factor 1)
• The urban background was the fourth of the
factors identified (factor 2).

6. Air quality effects from the construction site
• Different activities from the construction site
were identified as they were associated with
different particle profiles
• PM exceedances were found with strong
southern winds from either side of the
construction site even at night-time.

7. Tarmac Mountsorrel Quarry
• The second site analysed is a large granite
quarry in Leicestershire.
• Several activities take place at the quarry
having a different effect on the air quality
• The quarry is located south of the village
• Four unique particle profiles were identified
at the site.

8. Quarry dataset analysis
• Two of the factors identified are associated
with the works on the quarry (factors 2 and
3).
• The second factor pointing at dust related
works has a similar particle profile with the
one at Curzon street.
• The other two factors are associated with
background sources at the site, as well as
a source directly north of the monitoring
location

9. 45%
9%
17%
22%
4%
3%
Quarry air quality contribution
• The effect of the quarry on the measuring
site has been calculated and was found to
be significant only on strong southwestern
winds. This occurred only for a small
fraction of the period studied for which the
PM concentrations breached the latest
guidelines set by WHO in 2021.
• In general, the quarry impacted the air
quality at the measurement site, but this
effect is not significant and mainly with
larger particles.

10. PM Monitoring
• We calibrated the fugitive
emissions model adjusting
AP42 factors using the
difference between the on-
site and off-site monitors

11. Impact assessment
• PM2.5 impact using
calibrated fugitive emissions
factors

12. Steps to automation
Data collection from multiple
sites
Statistical analysis of the data
Separation of the pollution
sources and association with
unique particle number size
distribution (PNSDs) profiles.
Generation of a large repository of
PNSDs and pollution sources
combinations
Pinpoint the
possible sources of
a given PNSD
found at a given site
and time referring to
the repository
generated.
Reverse search
into datasets to
find the known
PNSDs of the
sources
anticipated
Using machine
learning

13. Conclusions and future work
• The method works and provides the separation of
the sources that affect the air quality at the given
sites
• The method also provides the expected levels of
pollution with different environmental conditions
(mainly with different wind conditions)
• The method has made a small contribution to an
EIA
• Good for establishing fugitive emission factors for
dispersion modelling
• AIPS project for bioaerosols has just started with
Met Office/EA/UKHSA

14. Questions?