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The London hyperlocal air quality monitoring network - Jim Mills
1. Hyperlocal Air Quality Monitoring Network
Jim Mills โ Managing Director โ Air Monitors Ltd
2. Breathe London - in 3 parts
MOBILE
AREA
COVERAGE
WEARABLES
REAL LIFE
EXPOSURE
STATIC
TEMPORAL
RESOLUTION
Courtesy - Dr Ben Barratt (Kings College London)
3. Google
Street View Cars
โข High accuracy and precision data,
due to reference grade
instrumentation used
โข Provide excellent spatial coverage of
the city
โข Identify โhot spotsโ of high pollution
โข where pods can then be located
to provide additional temporal
data
4. Google Cars
Instrumentation
โข Data is collected from
each instrument every
second and sent via cellular
networks to the cloud. It is
then available to the
project partners in near
real time for processing,
analysis and visualisation.
5. Google Cars
Measurements
โข Measuring NO, NO2, O3, CO2, PM10, PM2.5,
PM1, Pn, Nano Particles (LDSA) & Black
Carbonโฆplus GPS, T, H & P
โข Measurement recorded every second
โข (A measurement every 10-30m at city speeds)
โข Cars run 24/7 and are garaged at NPL,
Teddington where they are checked and
calibrated every week
6. Google Cars Parked at NPL Teddington
Courtesy of Dr Nick Martin, Stefan Bell and Joe Hayward (NPL)
7. Google Street View Car โ 1 second data output
Courtesy โ Prof R L Jones (University of Cambridge)
11. Calibration
QA/QC
Network
Management
Each individual sensor is calibrated in the lab using dry gases at
high concentrations
Each pod (family of sensors) is calibrated for at least one week
against reference monitors in ambient air before shipping
Pods are compared against each other at reference sites in the
city to ensure agreement with each other and the reference
Offset and slopes adjusted (if required) after regression analysis
Pods deployed at their network monitoring locations across the
city
One pod at each reference location held back and designated
โGoldโ (transfer standard)
Gold pods are then used for co-location studies at several pod
network sites to re-verify candidate pods over time.
BIG DATA analysis techniques used to identify possible outliers
which are then investigated further by the field team
19. 100 pods = BIG DATA
โข 310 electrochemical, 100 optical &
300 solid state sensors
โข Approx. 50 channels of information
from each pod every minute
โข 3,000 individual readings per hour
per pod
โข 300,000 readings per hour across
the city
โข 7,200,000 readings per day
โข 2.628 BILLION readings per year
20. โข Inform the Politicians & Public
โข Engage the Politicians & Public
โข Encourage Behaviour Change
โข Measure effectiveness of
interventions (e.g.ULEZ)
โข Drive continual improvement
in air quality in the city
โข Replicate what we learn from
London in other cities
Thanks to
the GLA and
project
partners for
their support
and guidance