Title: Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

Authors: David Carruthers; Amy Stidworthy; Daniel Clarke; Jo Dicks; Rod Jones; Ian Leslie; Olalekan A.M. Popoola; Martin Seaton

Addresses: Cambridge Environmental Research Consultants, 3 Kings Parade, Cambridge, CB2 1SJ, UK ' Cambridge Environmental Research Consultants, 3 Kings Parade, Cambridge, CB2 1SJ, UK ' Cambridgeshire County Council, Shire Hall, Castle Street, Cambridge, CB3 0AP, UK ' Cambridge City Council, Mandela House, 4 Regent Street, Cambridge, CB2 1BY, UK ' Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK ' The Computer Laboratory, Department of Computer Science and Technology, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK ' Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK ' Cambridge Environmental Research Consultants, 3 Kings Parade, Cambridge, CB2 1SJ, UK

Abstract: An optimisation scheme has been developed that applies a Bayesian inversion technique to a high resolution (street-level) atmospheric dispersion model to modify pollution emission rates based on sensor data. The scheme minimises a cost function using a non-negative least squares solver. For the required covariance matrices, assumptions are made regarding the magnitude of the uncertainties in source emissions and measurements and the correlation in uncertainties between different source emissions and different measurement sites. The scheme has been tested in an initial case study in Cambridge using monitored data from four reference monitors and 20 AQMesh sensor pods for the period 30 June 2016 to 30 September 2016. Hourly NO_x concentrations from road sources modelled using ADMS-Urban and observed concentrations were processed using the optimisation scheme and the adjusted emissions were re-modelled. The optimisation scheme reduced average road emissions on average by 6.5% compared to the original estimates, changed the diurnal profile of emissions and improved model accuracy at four reference sites.

Keywords: inversion; optimisation; emissions; ADMS-Urban; sensors.

DOI: 10.1504/IJEP.2019.104878

International Journal of Environment and Pollution, 2019 Vol.66 No.4, pp.252 - 266

Accepted: 26 Apr 2019
Published online: 05 Feb 2020 *

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