Air Monitoring 37


10ppm (AQMesh) and 0.6 ppm (LI-COR) and the background uncertainty was 10ppm, since this was derived from an AQMesh measurement. The emissions uncertainties were set as 100% and 150% of a priori emission rates for roads and other emission sources respectively. Uncertainty covariances as a percentage of uncertainty were 5% between the same measurement devices (otherwise zero), 40% between road source emission rates and 20% between other emission sources.


Results ADMS-Urban was used in ‘forward mode’ to model hourly CO2 concentrations at the sensor


locations for the period 1 July until 31 December 2021. Then the inversion scheme was applied to assimilate the measurements with the modelled data, resulting in adjusted hourly CO2


emission rates for sources across the city. As an example of the model output,


Figure 2 shows a comparison between the model predictions and measurements of CO2 concentrations at site C11 before and after the emissions were adjusted. C11 is a roadside site south east of Glasgow and west of the M74 motorway. The plots show the contribution of the background, road emissions and other emissions to total concentration. The gap between the coloured region and the black line represents the difference between the modelled and observed levels of CO2


. Using the adjusted emissions, it is seen that the gap is reduced by changes in both the road (reductions) and non-road (increases) emissions. Figure 2. Comparison of observed and ADMS-Urban modelled hourly CO2 concentrations, at urban traffi c site C11.


Figure 3 shows scatter plots comparing the monitored and modelled hourly average CO2 concentrations across all the AQMesh and LI-COR sites before and after data assimilation. These plots show that the inversion scheme improved the model predictions, more especially at the locations of the LI-COR instruments, as these instruments have lower measurement uncertainty.


Figure 4 shows an example of the changes in emission rates arising from the application of the inversion scheme, in this case for road emissions only. It is seen (left-hand plot) that the model estimates that, overall, the a priori road emissions are somewhat overestimated (2.1%), with a larger overestimate to the east of Glasgow and a smaller underestimate to the west. Increasing the specifi ed measurement uncertainty in the LI-COR (right plot) decreases the asymmetry in the plot between east and west, but retains a similar overall overestimate. This overestimate is to be expected as no allowance was made for the impact of COVID on traffi c fl ows in the a priori emissions; in 2021 these were still somewhat depressed. The model suggested emissions of other sources (not shown in the fi gure) were overestimated by 2.9%.


Conclusions


Previous studies have applied the assimilation scheme to the adjustment of emissions of toxic air pollutants. This initial study for CO2


emissions, demonstrates the potential of this


data assimilation technique as a powerful tool for verifying the accuracy of greenhouse gas emissions inventories using ambient measurements. Overall the study suggests that in this case the CO2


emissions inventory needed little adjustment during this period, though some


features of the emissions results need further investigation (e.g. sensitivity to specifi ed emission and measurement uncertainties, and effects of biogenic emissions and sinks).


Currently the assimilation technique treats each hour independently. Future developments will allow for correlations of emissions over different hours for example successive hours or the same hour each day. They will also refi ne the a priori estimates of uncertainties of emissions and measurements and their correlation, and, in the case of CO2


, take account of the large


biogenic sources and sinks which are highly variable depending on both the land surface and time.


Figure 3. Scatter plots comparing monitored and modelled hourly average CO2 and LI-COR sites before and after data assimilation.


concentrations (ppm) across all AQMesh References


Carruthers D, Stidworthy A, Clarke D, Dicks J, Jones R, Leslie I, Popoola OAM and Seaton M, 2019: Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques. International Journal of Environment and Pollution, vol. 66, issue 4, pp. 252-266, DOI: 10.1504/IJEP.2019.104878.


Hood C, MacKenzie I, Stocker J, Johnson K, Carruthers D, Vieno M and Doherty R, 2018: Air quality simulations for London using a coupled regional-to-local modelling system. Atmospheric Chemistry and Physics, vol. 18, pp. 11221-11245, DOI: 10.5194/acp-18-11221- 2018.


Figure 4 Difference in average gridded road emission rate (posterior – prior). Grid cells are shown where they are adjusted by the model assimilation for more than 300 hours in the period 1 July - 31 December 2021.


Tsagatakis, I., Richardson, J., Evangelides, C., Pizzolato, M., Pearson, B., Passant, N. & Otto, A. (2020) UK Spatial Emissions Methodology: A report of the National Atmospheric Emission Inventory 2018. Retrieved from: https://naei.beis.gov.uk/reports/reports?report_id=958 Stidworthy A, Carruthers D, Oades M, McCosh G, Jones R, Popoola O, Mills J, Sharp F, 2022: Quantifying the Impact of COVID-19 Restrictions on Emissions using Inverse Modelling and Measurements. 21st International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Aveiro, Portugal, September 2022. Article online


Author Contact Details David Carruthers1 • 1


, Amy Stidworthy1


, Molly Oades1 Scotswolds, UK


, George McCosh1


CERC, 3 King’s Parade, Cambridge, CB2 1SJ, UK, 2 Cambridge, CB2 1EW, UK, 3


, Rod Jones2


, Olalekan Popoola2


and Jim Mills3 Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfi eld Road, Email: david.carruthers@cerc.co.uk • Web: www.cerc.co.uk WWW.ENVIROTECH-ONLINE.COM


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