Routes to Clean Air 2016 - Dr Kevin Turpin, TRL

© Copyright 2016 TRL Ltd
Dr Kevin Turpin
October 2016
Development of Remote Sensors for
Vehicle Emissions Detection

Contents
2
• Background
• Idea
• Objectives
• Approach
• Results
• Conclusions
• Applications
• Follow on research

Background
1. Past 20 years: Little sustainable improvement in AQ
2. Trend data shows that NO2 concentrations at roadside locations in Inner
London Boroughs have barely improved since the early 2000s (Policy Exchange 2016)
3. Road transport is responsible about 46% of total NOX (Transport Professional 2015)
4. Road transport contribution to NO2 can be as high as 80% at some near road
locations (Transport Professional 2015)
5. Primary NO2 emissions from diesel vehicles are a key issue
6. Vehicle NO2/NOX ratio has increased over the decade. Some evidence
perhaps that f-NO2 is now stabilising (Carslaw et.al. 2016)
7. Devices fitted to vehicles to control emissions may not be as effective as one
might expect particularly on light vehicles
8. One solution perhaps to map and manage NO2 is to further develop remote
sensors
9. Provide better evidence for national and local air quality management (e.g.
AQMAs)
3

Initial Idea
FEAT
(20 years old)
HC+CO+CO2
+NO
IR/UV
technology
Adding
NO2+NH3
(1Year+£30K)
?
Cambridge
University
Department
of Chemistry
LEDs and
Differential
optical
absorption
spectroscopy
Potential for
a state-of-
the-art
upgrades
Awarded DfT
Innovation
Grant
Low end
Accuracy
option
High end
Accuracy
option

Objectives
1. Construct a prototype lab based NO2 DOAS measurement system at Cambridge
University;
2. Carry out laboratory sensitivity tests of the NO2 system;
3. Calibrate FEAT to measure CO, HC and CO2 concentrations in controlled
conditions at TRL;
4. Undertake field trials of the NO2 DOAS system alongside FEAT under controlled
conditions at TRL;
5. Analyse NO2 results from the DOAS system with fast response CO2
measurements measured by FEAT;
6. Provide recommendations for future commercial evolution of the system
including potential developmental funding opportunities.

Approach
Construct a prototype lab based NO2 DOAS measurement system
 The key to this system is the application of a bright and stable LED which is much
more robust and compact than conventional IR and UV light sources (e.g. those
used in FEAT).
 Key issue: Whether the light level observed through the detector is acceptable for
sensitive NO2 measurements
 Results from the bench test proved successful!
Blue LED source
Adsorption detected by
sensor
Principal of the lab testing

Approach
Carrying out laboratory sensitivity tests of the NO2 system
 Comparisons between the spectrum outputs were made with what is expected
from a known concentration of NO2
 20 ppm concentration of NO2 was injected to a Tedlar gas sampling bag (with a
depth of 5 cm along the direction of the beam, similar to that of a typical tailpipe
plume) and then placed in the middle of the light beam.

Approach
Setting up and calibrating FEAT
 Convert raw voltage output values to concentrations as a percentage of exhaust
gas
 No obvious way of understanding this process
 To overcome this issue, TRL undertook a series of tests to calibrate the FEAT
system in the laboratory
 The calibration cell used for this experiment
was 20 mm long by 25 mm in diameter and
the lens was made from quartz.

Approach
Performing field trials of the NO2 measurement system alongside FEAT under
controlled conditions at TRL
 Two phases of field trials were conducted for this study using controlled hanger
facilities
 FEAT and NO2 measurement system were set up alongside each other at the
same height with the light sources aligned
Recording CO-HC-CO2 & NO2

Approach
Performing field trials of the NO2 measurement system alongside FEAT under
controlled conditions at TRL
 10 vehicles (mainly diesels were used in the testing phase)
 Three drive-bys for each vehicle

Results
NO2 measurement system calibration results
 Upper panel: Calibration at 40 Hz (grey dots), 10 Hz (green lines), and 1 Hz (black lines)
acquisition rate. Lower panel: Probability distribution of a series of zero-ppm measurements
made at 40 Hz.

Results
 FEAT calibration results (US data)
High peaks in NO2 readings were recorded
Source: FEAT System US

Results
FEAT calibration results
 This test allows the team to understand the voltage output with respect to the
change in pollutant concentration
 1% change in CO2 causes the FEAT output to change by 73.1 mV

Results
 FEAT calibration results (initial drive-by test)
 Shows the initial blockage of light by the vehicle and then the trace recovering
back to a steady state (e.g. ~5,250 mV for CO2)
Steady state
0 0.120.04 0.06 0.08 0.10.02
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Time (s)
Source
reading
(mv) Vehicle detected

Results
Laboratory drive-by testing (Phase 1)
 Measured and reference spectra for NO2 from one of the vehicle passes. This
confirms an absolutely unambiguous NO2 identification

Results
 The observed NO2 from a 2005 diesel van and a 2010 eco-diesel passenger car are
presented.
 The FEAT system recorded % CO, HC and CO2
Conclusions from the NO2 measurement system:
 The two vehicles have similar NO2 emissivity;
 The same vehicle can show significantly different particle emissivity (e.g. Pass 1
and 3, for the older 2005 van);
 Diesel Eco was no better in terms of NO2 emissivity, but it seems that fewer
particles were emitted during its two passes;

Results
 The data from the NO2 measurement system were also calculated to provide NO2
concentrations based on the assumed exhaust plume width (i.e. 8cm). The
average reading for each vehicle tested is given below

Results
 Laboratory drive-by testing (Phase 2)
 Data were logged from the FEAT and NO2 measurement system and analysed together to provide
results for HC, CO and NO2 in terms of percentage ratio with CO2 for each vehicle tested
 NO2 emission factors can then be derived

Conclusions
 Unequivocal detection of NO2, identified by its unique spectral signature as
demonstrated clearly in this presentation against data reported in the literature.
 Demonstrating from laboratory tests that the sensitivity level required for the
project has been achieved
 Controlled field trials that have demonstrated real world detection of NO2 using
the optical sensor alongside the existing proven FEAT technology from a range of
different moving vehicles
 Integration of fast response (40Hz) DOAS NO2 measurements with those from the
FEAT system enabling NO2 emission factors to be derived for a range of vehicles
 Demonstrates the DfT’s priorities to better characterise emissions from vehicles in
real-time and goes a step further in developing a measurement system that has
the potential to better characterise the primary NO2 fraction from vehicle
exhausts.

Applications
There are a number of potential applications;
 Identifying high emitters on the road so that they can be rectified
 Apportionment of the NO2 emissions to difference vehicle types (car, taxi, LGV, HGV,
bus etc.) so that the higher contributors can be targeted
 Checking the performance of a fleet of vehicles – e.g. bus emissions measured at the
depot
 Identifying new vehicle models which have suspicious NO2 emissions, which could be
selected for the type approval market surveillance testing
 Conducting ad hoc on-road emission testing, of fleets or individual vehicles, e.g. at
ports or off slip roads
 With the addition of a NO channel, the NO2 proportion of NOx (f-NO2) could be
investigated at different locations

Follow on research
DfT T-TRIG Grant;
 Demonstration of proof of concept of a prototype low cost optical pollution
sensor
Why do we need these devices?
 Because existing monitoring using real-time or passive methods are often not
sufficient to adequately assess emission impacts and existing optical NO2
measuring devices are expensive, bulky and require mains power supplies.
The aim of the research:
 Provide proof of concept of an optical measuring device that uses the same
principles but is low cost, potentially smaller and battery powered.

Follow on research: challenges
The research will need to better understand;
 A range of spectrum and wavelengths
 Attenuation of signal and frequency of data loggers
 Power supplies
 Pollutant path lengths
 Proportion of light detected & detectable limits
 Etc.
One application: Real time AQ
impact assessment

Thank you for listening
Dr Kevin Turpin
email: kturpin@trl.co.uk

Routes to Clean Air 2016 - Dr Kevin Turpin, TRL

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