09.15Measuring air pollutant emissions using novel techniques.pdf

1. measuring vehicular pollutant
concentrations and emissions using
novel techniques
Professor Francis Pope
School of Geography, Earth and Environmental Sciences
University of Birmingham
f.pope@bham.ac.uk
www.francispope.com

2. Acknowledgements

3. Rationale
• There are increasing number of tools to measure air pollutant
concentrations and air pollutant emissions
• Some are direct measurements and some measure proxies that can be
related to air pollution
• This talk will look at air pollution concentration and emissions estimates
using:
• Remote sensing
• Telematics data
• Sensor data (both air pollution and noise)

4. Regulatory air quality monitoring networks
https://uk-air.defra.gov.uk
• e.g. AURN network
• Current sites: 171
• Data availability: 01/07/1972 to 05/12/2022

5. Remote Sensing:
On-road real-world measurements; EDAR

6. EDAR 2022 campaign, over 77,000 measurements, different vehicles subsets

7. Remote Sensing:
On-road real-world measurements; EDAR
Ghaffarpasand et al. (2020). Real-world assessment of vehicle air pollutant emissions subset by vehicle type, fuel and EURO class: New
findings from the recent UK EDAR field campaigns, and implications for emissions restricted zones. STOTEN
https://doi.org/10.1016/j.scitotenv.2020.139416

8. Urban Mobility in West Midlands
Number of Segments: 353,579
Road Lengths: 17,746.46 km
Total road lengths in England: 305,000km in 2020
Years of study: 2016 and 2018
Temporal resolution: 35 time slots (five days of the
week, seven time slots of a day)
Type of roads:
• Primary Roads
• Secondary Roads
• Motorways
• Residential
• Trunk
• Services

9. Telematics Methodology
Telematics data provides average
vehicular speed for a given time and
location
From this acceleration and vehicle
specific power can be calculated.
From this fuel consumption, and
exhaustive and non-exhaustive
emissions can be calculated
Driving behaviour characteristics can
also be explored
Ghaffarpasand et al, 2022. Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review. Sustainability, 14(24),
p.16386. https://doi.org/10.3390/su142416386

10. The West Midlands is not flat…

11. High spatial resolution

12. Highly detailed maps of urban mobility

13. Average Speed, Primary and Secondary roads
TS1=0-6
TS2=7-8
TS3=9-11
TS4=12-13
TS5=14-15
TS6=16-18
TS7=19-23

14. Vehicular Specific Power, the impact of the road slope
Without Slope
With Slope
TS1=0-6
TS2=7-8
TS3=9-11
TS4=12-13
TS5=14-15
TS6=16-18
TS7=19-23

15. Sensor data to measure and apportion air
pollution
A combination of mobile and static
sensor data allows to build up a
spatial and temporal understanding
of air pollution.
The RI-Urbans project has been using
a citizen science approach in Selly
Oak area of Birmingham.
Emission sources can be extracted
from data using source
apportionment techniques (see
Gordon Allison @DustScan’s talk)

16. Sensor data to measure and apportion air
pollution
A combination of mobile and static
sensor data allows to build up a
spatial and temporal understanding
of air pollution.
The RI-Urbans project has been using
a citizen science approach in Selly
Oak area of Birmingham.
Emission sources can be extracted
from data using source
apportionment techniques (see
Gordon Allison @DustScan’s talk)
Bousiotis et al. 2023. Towards comprehensive air quality management using low-cost sensors for pollution source
apportionment. npj Climate and Atmospheric Science, 6(1), p.122. https://doi.org/10.1038/s41612-023-00424-0

17. Sensor data to study traffic noise (TrafficEar)
The Traffic Ear sensor pack
determines the engine noise of
each passing vehicle without
interrupting traffic flow.
The device consists of an array
of microphones combined with
a computer vision camera. The
class and speed of passing
vehicles were estimated using
sound wave analysis, image
processing, and machine
learning algorithms.

18. Sensor data to study traffic noise (TrafficEar)
TrafficEar co-located with the EDAR data
collection to benefit from traffic counts and
vehicle identification

19. Sensor data to study traffic noise (TrafficEar)
Ghaffarpasand et al. 2023. Traffic Noise Assessment Using Intelligent Acoustic Sensors (Traffic Ear) and Vehicle Telematics
Data. Sensors, 23(15), p.6964. https://doi.org/10.3390/s23156964
Estimated contribution of diesel and petrol cars
estimated

20. Sensor data to study traffic noise (TrafficEar)
Ghaffarpasand et al. 2023. Traffic Noise Assessment Using Intelligent Acoustic Sensors (Traffic Ear) and Vehicle Telematics
Data. Sensors, 23(15), p.6964. https://doi.org/10.3390/s23156964
Average vehicle noise map (per-vehicle basis) of the West Midlands

21. Digital Twin Architecture
Digital Twins are systems that monitor
and control real systems in order to
achieve a desirable outcome (Goal).
Digital Twins manage a model of an
aspect of the real system to compare
real sensor data against an ideal
situation.
Digital Twins offer services to
stakeholders.
Digital Twins can be used for:
• what-if scenario playing.
• if-what policy creation.
• adaptive control.
Real
System
Digital
Twin
actuator
sensor
Goal
System
Model Services
Stakeholder
Digital Twin: Conceptual Architecture

22. Ethics and Data
• Privacy Concerns - Sensors collect data that can inadvertently reveal personal
information about individuals or their activities, potentially compromising their
privacy.
• Location Tracking - Sensor data can be used to infer the location of individuals, raising
concerns about location tracking without their consent, which could be exploited for
surveillance or other nefarious purposes.
• Health and Sensitive Data - Sensors may inadvertently capture health-related data,
such as respiratory conditions, potentially revealing sensitive information about
individuals' health status without their knowledge or consent.
• Behavior and Habits - Analysis of sensor data can reveal patterns in people's behavior
and daily routines, such as when they are at home or work, which can be exploited by
malicious actors or advertisers to target individuals.
• Discrimination and Bias - The use of sensor data may unintentionally reinforce
existing biases, such as disproportionately affecting marginalized communities or
exacerbating environmental injustices, which raises ethical concerns related to
fairness and equity.

23. Summary
• Many varied devices can now give information on air pollution
concentrations and emissions
• Through combination of these different data streams, high spatio-temporal
information on air pollution sources, emissions, and concentrations can
now be generated.
• Combining these data streams together is still in its infancy, but offers
great potential -> e.g. digital twins
• High resolution data increasingly also brings ethical concerns