Lekan Popoola Department of Chemistry University of Cambridge

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Low-cost sensor platforms: an emerging tool for air quality studies

Lekan Popoola

Department of Chemistry

University of Cambridge

oamp2@cam.ac.uk

Outline of Presentation

• Low-cost sensors

– Philosophy of approach

• Examples of ambient low-cost AQ network deployments

– SNAQ London Heathrow airport project

– Air Pollution and Human Health (APHH) Beijing project

– Breathe London (BL) project

• Conclusions

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Low-cost air quality sensor networks: personal and outdoor nodes

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Sensor Network for Air Quality (SNAQ) London Heathrow

airport project

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Use of sensor network…..

Sensors have very good

correspondence with conventional

instruments

unique emission patterns

linked to airport

operations

• Gas species (CO, NO, NO2, OX,

SO2, TVOCs & CO2)

• Size speciated PM (0.38 – 17.4µ)

• Temperature, RH, wind speed

and direction

• All 20s data

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Combine with meteorology: source apportionment

N

Sensor nodes

Runway

Categorise by

wind speed and

direction

Perimeter road

Traffic source

(perimeter road)

Aircraft source

(runway)

(© OpenAir!)

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Use network to separate local and non-local sources

source location

Network – all sensors

(non-local)

CO2

Single sensor

(local only)

Single sensor

(local + non-local)

CO2

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CO NO NO2 CO2

Characterisation of pollution sources

Local (single SNAQ node)

NO

2(p

pb

)

Baseline: NO2 only (network response): easterly direction

Features

• London plume

• Road traffic

diurnal profile

diurnal

Pollution predominantly from long range transport (road traffic)

Pollution from

easterly directionRoad traffic

diurnal pattern

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Observational constraint of model emission indices:

ADMS (model) SNAQ (measurements)

CO NO

NO2

ADMS

model

(CERC)Δspecies

ΔCO2

Very good

agreement between

observation and

model at different

receptor points

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Air Pollution and Human Health (APHH) Beijing project

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• × 25 deployed in Beijing

✓ × 6 (8, 32, 100, 160, 260, 320 m )

on IAP meteorology tower

✓ × 18 as a network in the city

✓ × 1 co-located with York container

• × 4 in Pinggu (rural location)

SNAQ deployment details

8 m

32 m

102 m

160 m

260 m

320 m

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summerwinter

Winter – Summer Contrast

⇒ Winter dominated by elevated CO, NOX, PM (BL stability and

long-range transport) & lower OX (chemistry)

⇒ Summer characterised by higher OX (chemistry dominated)

and lower CO, PM, NOX (unstable BL)

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Breathe London project

https://www.breathelondon.org

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Breathe London static sensor network

https://www.breathelondon.org

100+ AQMesh

pods NO2, NO,

CO2, (O3), PM

Key differences:

• 1 min data (mostly!)

• CO2 measurements

Not reference!

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Diurnals (average and day-of-week) and mean (day of week and monthly) profiles for absolute NOX

AQMesh vs. LAQN & AQE network: April – December 2019

AQMesh network (88 nodes)

AQE network (50 stations)

LAQN network (86 stations)

AQE

LAQN

AQMesh

vs.

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Emission ratio determination

NO2 (NOx) CO2

Scale separated NO2, NO

and CO2

Extract local NOx, CO2

Take point by point ratio

(1 min)

Derive ERNOx(t, x, y)

Gradient is

emission ratio

Scale separation now allows local emission ratios to be derived:

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All 88 sitesUrban background

Kerbside & roadside

Kerbside

Roadside

ULEZ (× 22)

Non-ULEZ (× 66)

ULEZ (× 7)

Non-ULEZ (× 25)

ULEZ (× 15)

Non-ULEZ (× 41)

ULEZ (× 6)

Non-ULEZ (× 23)

ULEZ (× 9)

Non-ULEZ (× 18)

All sites partition by

ULEZ and non-ULEZ

Disaggregation of NOx emission ratios

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UK Lock down impacts: NO2?

Model (ADMS) vs Observations (LAQN & BL)

Lockdown

NO

2(µ

g/m

3)

LAQN network average

BL network average

ADMS model (BL network)

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• A reduction in road traffic NOX emissions to around 10-20% of pre-lockdown levels.

• Modelled as ~ 86% reduction in mean traffic emission compared to pre-lockdown

Evaluation of UK lockdown using data assimilation into ADMS model

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Summary

❑ low-cost sensor technology for air pollution, viable for……..

• both in developed and low-income countries.

• personal exposure studies.

❑ applications

• Source apportionment

• Emission ratios

• assimilation of sensor network data for improved modelling

Already adding value……..

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Thank you for your attention

Contributing partners

Lekan Popoola, Geoff Ma, Le Yuan, Vivien Bright , Iq Mead, Lia Chatzidiakou, Ray Freshwater, and Rod Jones

(Cambridge team)

David Carruthers , Chetan Lad, Amy Stidworthy, Ella Forsyth and Mark Jackson (CERC team)

Ramon Alvarez, Dan Peters, Megan Dupuy-Todd, Elizabeth Fonseca (EDF Team)

Jim Mills, Felicity Sharp (ACOEM /Air Monitors)

Nick Martin (National Physical Laboratory)

Spencer Thomas, Luke Cox, David Vowles (HAL/BAA, BA for airport logistics)

Paul Kaye (and University of Hertfordshire team)

John Saffell (Alphasense, UK)