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

Transcription

1 Using low-cost sensor networks to refine emissions for use in air quality modelling Amy Stidworthy, David Carruthers, Chetan Lad & Jenny Stocker FAIRMODE Technical Meeting June 2017 Athens Greece

2 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

3 Positive and negative aspects of sensors Low cost sensors Traditional measurement techniques Cost Variable, but generally low High Accuracy Spatial resolution Human exposure Less reliable & generally less accurate High & possibilities for indoor & outdoor measurements Suitable for personal exposure measurements i.e. people carry them Reliable & usually accurate Low Measurements may not be representative of people s exposure What do they have in common? Record O 3, CO, NO, NO 2, SO 2, CO 2, total VOCs and PM Use different techniques for gaseous and particulate pollutants Require calibration Sensor techniques metal oxide or electrochemical sensing optical detection and other methods

4 Using sensors to optimise AQ modelling results 1 Restrictions: Sensor monitored concentration data 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) 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 RUN 1: local AQ model (ADMS-Urban) with standard emissions Apply inversion technique # to calculate adjusted emissions + RUN 2: local AQ model (ADMS-Urban) with adjusted emissions Evaluate against reference monitors # Probabilistic approach following work by others, e.g. Webster et al, T 1 T 1 J x Mx y R Mx y x e B x e - refer to full presentation (link on FAIRMODE last slide) 2017

5 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. 5 reference monitors % University of Cambridge, Cambridge City & County Council, FAIRMODE AQMesh 2017

6 Modelled concentrations Effect of optimisation on model validation Frequency scatter plot: hourly NOx, reference monitors only 1. Base 2. All monitors 3. Sensor network only Data points not included in the inversion Validation at reference sites only Observed concentrations 1. Base Base case model output 2. All monitors Model output using optimised emissions; optimisation carried out using all sensor data 3. Sensor network only Model output using optimised emissions; optimisation carried out using AQMesh data only Statistics Mean StDev Obs Mod Obs Mod MB NMSE R Fac

7 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 CO 2 ) 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

8 Summary 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 Link to full presentation on model optimisation using sensor data: quality-models-with-sensor-data?qid=bf79a79b-24b8-47b0-9d51-41f8fdb69019&v=&b=&from_search=3

9 Extra slides

10 Using sensors to optimise AQ modelling results 3 Regent St Parker St Evaluation against reference monitors: 5 th July 2016 Gonville Place Montague Rd Preliminary tests: currently sensors only located close to roads, and only modelling road sources Success: works at reference locations