networks to refine emissions for use in air quality modelling Amy - - PowerPoint PPT Presentation

Amy Stidworthy, David Carruthers, Chetan Lad & Jenny Stocker

Using low-cost sensor networks to refine emissions for use in air quality modelling

FAIRMODE Technical Meeting June 2017 Athens Greece

FAIRMODE 2017

Contents

• Positive and negative aspects of sensors
• Methodology for using sensors to optimise AQ

modelling results – Cambridge pilot study

• Using sensors to optimise aircraft emissions indices

for use in AQ modelling at Heathrow Airport

• Summary

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Positive and negative aspects of sensors

• What do they have in common?

− Record O3, CO, NO, NO2, SO2, CO2, total VOCs and PM − Use different techniques for gaseous and particulate pollutants − Require calibration Low cost sensors Traditional measurement techniques Cost Variable, but generally low High Accuracy Less reliable & generally less accurate Reliable & usually accurate Spatial resolution High & possibilities for indoor & outdoor measurements Low Human exposure Suitable for personal exposure measurements i.e. people carry them Measurements may not be representative of people’s exposure

metal oxide or electrochemical sensing

• ptical detection

and other methods Sensor techniques

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Using sensors to optimise AQ modelling results 1

RUN 1: local AQ model (ADMS-Urban) with standard emissions RUN 2: local AQ model (ADMS-Urban) with adjusted emissions Apply inversion technique# to calculate adjusted emissions+ Sensor monitored concentration data Evaluate against reference monitors Monitor data error: systematic (e.g. temperature dependence) + unsystematic (e.g. faults) Emissions data error: systematic (e.g. emission factors) + unsystematic (e.g. driving behaviour) Restrictions:

• Inversion technique requires model concentration to be

proportional to the emissions, so complex effects like local chemistry have to be ignored in RUN 1

• Sources included must influence at least one sensor
• Sensors included must have non-zero ‘traffic

concentration’

# Probabilistic approach following work by others, e.g. Webster et al, 2016 +

• refer to full presentation (link on last slide)

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e x B e x y Mx R y Mx x      

  1 1 T T

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Using sensors to optimise AQ modelling results 2

• Preliminary results: Cambridge

– CERC have been collaborating% on a project to study ambient air quality across Cambridge using a large number of sensor nodes and computer modelling. – 20 AQMesh sensor pods have been placed at key points around Cambridge, measuring air quality in near real time.

% University of Cambridge, Cambridge

City & County Council, AQMesh

– 5 reference monitors

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Effect of optimisation on model validation

Statistics 1. 2. 3. Mean Obs 31.2 31.2 31.2 Mod 34.5 29.3 31.3 StDev Obs 27.9 27.9 27.9 Mod 31.0 26.0 27.0 MB 3.30

• 1.91

0.10 NMSE 0.51 0.05 0.39 R 0.70 0.97 0.75 Fac2 0.71 0.94 0.73

• 1. Base

Base case model output

• 2. All monitors

Model output using optimised emissions;

• ptimisation carried out using all sensor data
• 3. Sensor network only

Model output using optimised emissions;

• ptimisation carried out using AQMesh data
• nly

Validation at reference sites only

Data points not included in the inversion

Observed concentrations Modelled concentrations

• 1. Base
• 2. All monitors
• 3. Sensor

network only Frequency scatter plot: hourly NOx, reference monitors only

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Using sensors to refine modelling at Heathrow Airport

• University of Cambridge & CERC, funded by NERC & EPSRC,

working with Heathrow Airport Ltd

• 17 sensors deployed at Heathrow Airport, 5-week period
• Results:

– Sensors are able to distinguish airport emissions from long range transport, leading to:

• Refinement of aircraft activity emissions (using ratios of sensor

measurements to CO2)

• Quantification of the relative importance of aircraft emissions & road

traffic emissions – Improved emissions used in modelling (ADMS-Airport) leads to very good agreement with measurements

• Methodology can be used for other applications e.g. traffic

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• Low-cost sensors may (or may not) have reliability issues,

but when deployed as networks, they can be used to: − Improve emissions calculated using ‘standard’ emission factor datasets − Identify the contribution of local sources compared to long-range transport

Summary

Link to full presentation on model optimisation using sensor data: https://www.slideshare.net/ies-uk/amy-stidworthy-optimising-local-air- quality-models-with-sensor-data?qid=bf79a79b-24b8-47b0-9d51- 41f8fdb69019&v=&b=&from_search=3

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Extra slides

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Using sensors to optimise AQ modelling results 3

Montague Rd Regent St Parker St Gonville Place

• Preliminary tests: currently sensors only

located close to roads, and only modelling road sources

• Success: works at reference locations

Evaluation against reference monitors: 5th July 2016