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Amy Stidworthy - Optimising local air quality models with sensor data - DMUG17

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An unapologetically technical conference, DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.

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Amy Stidworthy - Optimising local air quality models with sensor data - DMUG17

  1. 1. Amy Stidworthy DMUG 6h April 2017 London Optimising local air quality models with sensor data: examples from Cambridge
  2. 2. DMUG, London, 6th April 2017 Acknowledgements • The work presented here has been done by CERC based on ADMS-Urban modelling of Cambridge following the deployment of AQMesh sensors in Cambridge. Partners include: – Rod Jones & Lekan Popoola, Department of Chemistry, University of Cambridge – Dan Clarke, Cambridgeshire County Council, Cambridge – Jo Dicks & Anita Lewis, Cambridge City Council, Cambridge – Ian Leslie, Computer Laboratory, University of Cambridge – Amanda Randle, AQMesh
  3. 3. DMUG, London, 6th April 2017 Outline of presentation • Motivation • Optimisation technique • Preliminary results for Cambridge • Further work
  4. 4. DMUG, London, 6th April 2017 Motivation • Emissions errors account for a significant proportion of dispersion model error • Traditionally, dispersion models such as CERC’s ADMS-Urban model are validated against data from reference monitors: – Modellers either use the validation to improve model setup; or – Calculate and apply a model adjustment factor to model results • New low cost air pollution sensors allow large networks of sensors to be installed across a city • Accuracy and reliability is generally lower than reference monitors, but larger spatial coverage is possible • How can we best use these sensor data in modelling? • If the data are not accurate and reliable enough for model validation, maybe we can use the data in a different way...
  5. 5. DMUG, London, 6th April 2017 Optimisation technique: Overview • The aim is to develop an inversion technique to use monitoring data from a network of sensors to automatically adjust emissions to improve model predictions • Basic idea: – Run ADMS-Urban to obtain modelled concentrations at monitor locations in the normal way – Use these modelled concentrations and their associated emissions as a ‘first guess’, together with a) monitored concentration data b) information about the error in the monitored data and the proportion of that error that is systematic across all monitors c) Information about the error in the emissions data and the proportion of that error that is systematic across all sources – Use an inversion technique to calculated an adjusted set of emissions that reduces error in the modelled concentrations
  6. 6. DMUG, London, 6th April 2017 Optimisation technique: Introduction • There are some conditions that have to be satisfied for such a scheme to work: a) The model concentration must be proportional to the emissions, which means that complex effects like chemistry have to be ignored b) Any sources included must affect at least one receptor (monitor) c) Any receptor included must have non-zero concentration • The technique developed uses a probabilistic approach following work by others, for example as used by the Met Office for estimating volcanic ash source parameters using satellite retrievals [Webster et al, 2016]
  7. 7. DMUG, London, 6th April 2017 Optimisation technique: Cost function We define a cost function J(x) with two terms: one that describes the error in the modelled concentration (left-hand term) and one that describes the error in the emissions (right-hand term) The aim is to minimise J to obtain x, a vector of adjusted emissions. Quantity Definition Dimensions x Vector of emissions (result) n M Transport matrix relating the source term to the observations n by k y Vector of observations k R Error covariance matrix for the observations k by k e Vector of first guess emissions n B Error covariance matrix for the first guess emissions n by n          exBexyMxRyMxx   11 TT J
  8. 8. DMUG, London, 6th April 2017 Optimisation technique: Least squares problem • To solve the cost function minimisation problem, we first convert the problem to a ‘least squares’ problem, which is easier to solve computationally • A ‘least squares’ problem finds the best solution to the equation Ax=f, where x is a vector of size m, f is a vector of size n and A is a matrix with n rows and m columns. • The result of solving the least squares problem is the vector x that gives the minimum value of the sum of the squares of the elements of (Ax-f) • So, we need to write the cost function as Fast forward through the maths...      fAxfAx  T xJ
  9. 9. DMUG, London, 6th April 2017 Optimisation technique: Error covariance matrices • To solve the problem, we need to construct the matrix A and the vector f, but do we have all the information we need for this?  M is the transport matrix: this represents the contribution of every source to every receptor given a unit emission rate  y is the vector of monitored concentrations at each receptor  e is the vector of emissions for each source • What are the matrices T and D?  These are the related to the ‘covariance’ matrices R and B that represent the error in the monitored data and emissions data respectively.  The diagonal components of the covariance matrices represent the variance in the data, which is related to the uncertainty in the data;  The off-diagonal components represent how much of the error is ‘co- varying’, or in other words, systematic.                        eD yT f D MT AfAxfAx T T T T T xJ and
  10. 10. DMUG, London, 6th April 2017 Optimisation technique: Monitoring data error • The diagonal components of the monitoring data error covariance matrix R represent the variance σObs 2 of the monitored data, which is the square of the standard deviation σObs: – We assume that the standard deviation σObs is equal to the uncertainty in the monitoring data expressed as a concentration and is equal to Uobs x O, where Uobs is the uncertainty expressed as a fraction. • The off-diagonal components represent the error that co-varies between monitors, i.e. systematic error – We say that a given proportion of the uncertainty is due to systematic error
  11. 11. DMUG, London, 6th April 2017 Optimisation technique: Monitoring data error • So, for any two monitors labelled i and j, their covariance is defined as • The factor UfObs represents the fraction of the monitoring data uncertainty that is due to systematic error. • This raises questions: – How much of monitoring data error is systematic? – Should monitors of different types be treated as independent, with no co-variance? – Are there any causes of monitoring data error that affect all monitors, e.g. temperature, humidity? – Is there co-variance between sensors for different pollutants?                  jijyUUfiyUUf jiiyU ji ObsObsObsObs Obs Obs 2 2 ,
  12. 12. DMUG, London, 6th April 2017 Optimisation technique: Emissions data error • The diagonal components of the emissions error covariance matrix B represent the variance σEm 2 of the emissions data, which is the square of the standard deviation σEm: – We assume that the standard deviation σEm is equal to the uncertainty in the emissions data expressed as a concentration and is equal to Uem x E, where Uem is the uncertainty expressed as a percentage. • The off-diagonal components represent the error that co-varies between sources, i.e. systematic error – We say that a given proportion of the uncertainty is due to systematic error, for example traffic emissions factors
  13. 13. DMUG, London, 6th April 2017 Optimisation technique: Emissions data error • So, for any two sources labelled i and j, their covariance is defined as • The factor UfEm represents the fraction of the emissions data uncertainty that is due to systematic error. • This also raises questions: – How much of the emissions data error is systematic? For example, what proportion of road emissions data is due to errors in the emission factors (systematic) and how much is due to traffic counts (non-systematic) – Is there any co-variance in the emissions data error for different pollutants? PM10 and PM2.5 – yes, but PM10 and NOX?                  jijeUUfieUUf jiieU ji EmEmEmEm Em Em 2 2 ,
  14. 14. DMUG, London, 6th April 2017 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.
  15. 15. DMUG, London, 6th April 2017 Preliminary results: Cambridge • The aim of the preliminary Cambridge tests presented here is primarily to examine the behaviour of the optimisation scheme and refine the process, i.e. – Does it work?! – Is it practical? If it takes weeks to run then obviously not. – What effect does the choice of uncertainty parameters have on outcome? – How does the validation at the reference monitors change? – Can we learn anything about emissions?
  16. 16. DMUG, London, 6th April 2017 ADMS-Urban model setup • One source type: 305 road sources • One pollutant: NOX • 25 monitors: 20 AQMesh monitors and 5 reference monitors • Time-varying emission factors: diurnal profiles for weekdays, Saturdays and Sundays • Daylight saving option used to obtain correct emission factors • 3-month period: 30/06/2016 01:00 to 30/09/2016 23:00
  17. 17. DMUG, London, 6th April 2017 Optimisation process Step 1: Run ADMS-Urban to obtain modelled concentrations at monitoring site locations Step 2: Form the transport matrix, emissions vector and monitored data vector Step 3: Run the optimisation scheme Step 4: Create an hourly factors (.hfc) file from the adjusted emissions data Step 5: Re-run ADMS-Urban using the adjusted emissions .hfc file
  18. 18. DMUG, London, 6th April 2017 Optimisation parameters • As described previously, we specify the following parameters in the optimisation: EU Parameter name Description Uobs(ref) Observation uncertainty (reference monitors) Uobs(aqmesh) Observation uncertainty (AQmesh sensors) Ufobs(ref) Observation uncertainty covariance factor (reference monitors) Ufobs(aqmesh) Observation uncertainty covariance factor (AQmesh sensors) Uem Emissions uncertainty Ufem Emissions uncertainty covariance factor
  19. 19. DMUG, London, 6th April 2017 Optimisation Technique: Effect of uncertainty Optimisation is working! J is reduced for all uncertainty values Increasing Ou relaxes the constraints so J is reduced less
  20. 20. DMUG, London, 6th April 2017 AQMesh sensors: more model error toleratedRef: less model error tolerated 0 50 100 150 200 250 300 NOxconcentration(ug/m3) Observed Model (original emissions) Model (adjusted emissions, all sensor data) Effect of monitor uncertainty on concentrations • In these inversion calculations: – Reference monitor uncertainty set to 10% – AQMesh sensor uncertainty set to 30% – Covariance between Reference monitors (systematic error) set to 5% – Covariance between AQMesh sensors (systematic error) set to 10% – No covariance between Reference monitors and AQMesh sensors Example hour: 7am on 5th July
  21. 21. DMUG, London, 6th April 2017 Effect of emissions covariance on adjusted emissions -100% -50% 0% 50% 100% 150% 200% Percentage increase in source emission rate with different emissions error covariance settings Zero emissions error covariance 75% emissions error covariance • If emissions error covariance is zero, emissions can change completely independently • With non-zero emissions error covariance, emissions have to change more consistently across all sources
  22. 22. DMUG, London, 6th April 2017 Cambridge: optimisation parameters used Parameter name Description Value Uobs(ref) Observation uncertainty (reference monitors) 0.1 Uobs(aqmesh) Observation uncertainty (AQmesh sensors) 0.3 Ufobs(ref) Observation uncertainty covariance factor (reference monitors) 0.05 Ufobs(aqmesh) Observation uncertainty covariance factor (AQmesh sensors) 0.1 Uem Emissions uncertainty 0.5 Ufem Emissions uncertainty covariance factor 0.4 All monitoring data are provisional apart from Gonville Place reference monitor; AQMesh data were obtained in real time.
  23. 23. DMUG, London, 6th April 2017 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. Orig_RdsOnly Base case model output 2. Inv_ReRun_AllSensors Model output using optimised emissions; optimisation carried out using all sensor data 3. Inv_ReRun_AQMeshSensorsOnly Model output using optimised emissions; optimisation carried out using AQMesh data only Validation at Reference sites only Data points not included in the inversion
  24. 24. DMUG, London, 6th April 2017 Effect of optimisation on diurnal emissions profiles 0 0.5 1 1.5 2 2.5 0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22 Weekday Saturday Sunday Emissionfactor Diurnal emission factor profiles: original and adjusted emissions Original Adjusted, all sensors Adjusted, AQMesh sensors only
  25. 25. DMUG, London, 6th April 2017 Effect of optimisation on mean emission rates 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Meanadjustedemissionrate(g/km/s) Mean first-guess emission rate Optimisation using AQMesh sensor data only 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Meanadjustedemissionrate(g/km/s) Mean first-guess emission rate Optimisation using AQMesh sensor data and reference monitor data
  26. 26. DMUG, London, 6th April 2017 Effect of optimisation on mean emission rates Average % change -2.8% -60.0% -40.0% -20.0% 0.0% 20.0% 40.0% 60.0% Optimisation using AQMesh sensor data only Percentage change in mean emission rate per road source Average % change -3.0% -60.0% -40.0% -20.0% 0.0% 20.0% 40.0% 60.0% Optimisation using AQMesh sensor data and reference monitor data Including reference data causes big changes in just a few sources
  27. 27. DMUG, London, 6th April 2017 Example output at reference monitors: 5th July 2016 Montague Rd Regent St Gonville Place Parker St Newmarket Rd
  28. 28. DMUG, London, 6th April 2017 7-day average concentration: Adjusted - Original Example of how the optimisation process affects concentration contours: General reduction, but increase in some areas +30 -30 NOX ug/m3 0
  29. 29. DMUG, London, 6th April 2017 Discussion and further work • We have developed an optimisation scheme to use data from a network of sensors to automatically adjust emissions and thereby improve model results • Tests show that the scheme works and initial results are encouraging, but there is more work to do, for example: – More than 1 pollutant – Other source types • The optimisation scheme run times are also encouraging: approx 15 minutes to run 3 months of hourly data with 305 sources and 25 receptors, carrying out the optimisation for each individual hour • The values of uncertainty and covariance factors used so far are largely arbitrary; we need to use realistic values to obtain meaningful results • After Cambridge, the next step is to run the scheme with sensor data collected at Heathrow during the NERC SNAQ project.
  30. 30. DMUG, London, 6th April 2017 Thank you • Thanks again to CERC’s partners in this work: – Rod Jones & Lekan Popoola, Department of Chemistry, University of Cambridge – Dan Clarke, Cambridgeshire County Council, Cambridge – Jo Dicks & Anita Lewis, Cambridge City Council, Cambridge – Ian Leslie, Computer Laboratory, University of Cambridge – Amanda Randle, AQMesh • For more information about the ADMS-Urban dispersion model, see www.cerc.co.uk/Urban

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