During March–June 2017 emissions of nitrogen oxides were measured via eddy covariance at the British Telecom Tower in central London, UK. Through the use of a footprint model the expected emissions were simulated from the spatially resolved National Atmospheric Emissions Inventory for 2017 and compared with the measured emissions. These simulated emissions were shown to underestimate measured emissions during the daytime by a factor of 1.48, but they agreed well overnight. Furthermore, underestimations were spatially mapped, and the areas around the measurement site responsible for differences in measured and simulated emissions were inferred. It was observed that areas of higher traffic, such as major roads near national rail stations, showed the greatest underestimation by the simulated emissions. These discrepancies are partially attributed to a combination of the inventory not fully capturing traffic conditions in central London and both the spatial and temporal resolution of the inventory not fully describing the high heterogeneity of the urban centre. Understanding of this underestimation may be further improved with longer measurement time series to better understand temporal variation and improved temporal scaling factors to better simulate sub-annual emissions.
Nitrogen oxides (NO
London regularly faces issues with NO
101 air quality monitoring sites located in and around Greater London. Sites are coloured by their annual mean NO
According to the National Atmospheric Emissions Inventory (NAEI), road transport a well as domestic and industrial combustion are the key sources of NO
London's low emission zone (LEZ), introduced in 2009, aimed to improve air quality by reducing the pollution from heavy vehicles either by reducing their number or encouraging improved emissions control technology. This was shown to have reduced ambient NO
In April 2019 London introduced the ultra-low emissions zone (ULEZ) specifically targeting vehicle emissions. The charge applies at all times to vehicles that do not meet specific Euro classes for their vehicle type (motorbikes Euro 3, petrol cars Euro 4, diesel cars and larger vehicles Euro 6; 0.15, 0.08, and 0.08 g km
Whilst there are large numbers of ambient concentration measurements available, limited emissions measurements have been made in London. The NAEI provides UK-wide emissions estimates, and for Greater London they declined from 120 to 45 kt yr
Time series (left) and median diurnal profiles (right) of (top to bottom) measured of NO
Eddy covariance (EC) measurements of NO and NO
Airborne EC NO
We report on EC emissions measurements of NO
Measurements of NO and NO
A 3D ultrasonic anemometer (Gill R3-50) was mounted on a mast atop the tower, co-located with the gas analyser sample line inlet, providing a measurement height of 190 m. The anemometer provided 3D wind vectors and temperature derived from the speed of sound. Air was pumped down the
NO and NO
Eddy covariance calculations were performed using the
EC provides measurements of local flux at the receptor. These are related, but not identical, to the surface flux. This surface flux is what is comparable to the emissions inventories. The local flux can diverge from the surface flux due to the vertical separation. Turbulence properties are not vertically uniform through the boundary layer; as the top of the boundary layer is approached (the entrainment zone) vertical turbulent transport is reduced, turbulence properties are more disconnected from the surface, and the applicability of EC is diminished. This results in a vertical gradient of the turbulent flux: vertical flux divergence. This also results in concentration enhancements below the measurement height, causing a gradient throughout the boundary layer, and is described as storage flux. The flux not registered by the receptor can be estimated from either of these perspectives: from the rate of change in concentration with height (i.e. storage) or from proportionality with the entrainment height (i.e. vertical flux divergence). In the case of measurements made at 190 m above the surface, the measurement height is an appreciable proportion of the boundary layer height depending on the time of day and meteorological conditions. To account for this we apply a correction that assumes linear divergence of the vertical flux as a function of effective measurement height and effective entrainment height (Eq.
We apply this correction only when
Modelled boundary layer height data from the ECMWF ReAnalysis 5
The divergence has been assessed via this method due to the lack of gradient measurements available at the tower, and the single point correction as used by
We do not apply this correction to the data presented in this study due to the uncertainty in the boundary layer height, but in the best case of these calculations (corrected boundary layer height and constant flux layer term), the largest absolute change to the diurnal profile is 2.23 mg m
When flagging data for quality control, the stationarity criterion is more readily violated when the magnitude of the calculated flux is lower. Stationarity is considered violated if the flux calculated for a subsection of the aggregation period deviates from the flux calculated for the whole aggregation period by a predefined fraction (
In Fig.
To quantify the effect of removing the values, the diurnal profile for NO
Turbulent flow through the sampling line is a prerequisite for EC measurements. Laminar flow in the sample line causes the gas which interacts with the tubing wall to flow slower than that in the centre of the line, meaning that air parcels contain asynchronous samples, primarily causing high-frequency losses
In Fig.
Normalised co-spectra of vertical wind with NO (red, circle), NO
Due to its height the high-frequency contributions to fluxes measured at the BT Tower are expected to be small, with
High-frequency corrections derived for four sample line regimes using 0.3 and 1 Hz for the thresholds above which the correction was calculated.
The NAEI is an annual emissions estimate for a variety of species in the UK from 1970 to present. Commissioned by the Department for Environment, Food and Rural Affairs, it is currently produced by Ricardo Energy & Environment and used to report to European Union and United Nations greenhouse gas and air pollutant monitoring programmes
Once emissions estimates as a whole are compiled, the emissions are gridded using spatial information relevant to the SNAP sector. For example, road transport uses road network location, local fleet composition from automatic licence plate recognition statistics, and the annual average daily flow of traffic
The LAEI is an annual emissions estimate that has been produced periodically since 2006 covering Greater London at a 1 km
Four source sectors are included in the LAEI – transport, industrial and commercial, domestic, and miscellaneous. A notable difference here is the grouping of commercial sources with industrial, whereas in the NAEI they are grouped with domestic sources. The inventory used in this work was provided with hour of day scaling for the transport sector but has otherwise been treated the same as the NAEI.
To link the measured fluxes to the surface, we used the 2D footprint model by
The sum of the NAEI layers corresponding to SNAP sectors 07, 02, 03, and 08 (see Table
These hourly footprints were used to simulate an emissions time series from the spatially resolved NAEI for 2017 and LAEI for 2016. This was achieved by first extracting, on a by-sector basis, the inventory's grid cell (1 km
The footprints were also used to map the measured and expected emissions spatially. This was achieved using the
In Fig.
Comparison of these measurements with emissions inventories was performed by generating a simulated emission time series via the method described in Sect.
Figure
Median diurnal profiles of NO
By wind sector the story is more varied. The north and east show the measurements spiking significantly above the simulated emissions during the morning but then show good agreement throughout the rest of the day, again with the simulated emissions being higher at night. This is reflected in the daily medians of both of these sectors being much closer to unity (ratios of 1.13 and 0.99, respectively). In the south and west the daytime underestimation by the inventory can be observed (ratios of 1.54 and 1.53, respectively), whereas overnight the agreement is better than the overall average at 1.03 and 1.00, respectively. Table
Inventory sector contribution to simulated emissions by wind sector.
In Fig.
Average diurnal profiles of NO
Daily averaged measured NO
A
Ratio of measured to simulated NO
To further explore the differences in wind sector dependence of the emissions and begin to identify potential missing sources, Fig.
NO
In Fig.
The measured emissions (Fig.
Several other studies have measured urban NO
Diurnal profiles of measured NO
During March–June 2017 NO
The inventories underestimated (1.48 times) the measured NO
It is clear from these measurements that there are contributions to the NO
Resolving the measurements spatially does provide hints as to where the discrepancies may be found, and here we showed that the highest emissions around the tower were close to locations that experience high congestion. The change in traffic emissions due to congestion is not something that is directly parameterised in the bottom-up inventories used here, but further investigation may be able to close the gap between them and measurements.
Continued policy intervention in London, such as the implementation and expansion of the ultra-low emission zone as well as changes in short-term activity and long-term behaviours resulting from the COVID-19 pandemic which took hold in the UK in March 2020, will strongly affect NO
Schematic eddy covariance calculation workflow.
SNAP (Selected Nomenclature for sources of Air Pollutants) sector definitions as used in the NAEI
Visualisation of the vertical flux divergence corrections with respect to boundary layer height.
Hour of day scaling factors for the four SNAP sectors (07, 02, 03, and 08, see Table
Month of year scaling factors for the four SNAP sectors (07, 02, 03, and 08, see Table
The eddy4R v.0.2.0 software framework used to generate eddy covariance flux estimates can be freely accessed at
Data for Fig. 2 have been taken from the Automatic Urban and Rural Network (
For the measurement data in this paper, the calculated fluxes are not available in any repository due to the intensity of the post-processing and interpretation required. We are happy to make this available upon request.
15 min aggregated concentrations are available on the Centre for Environmental Data Analysis database, but these were not directly used here. The ERA5 boundary layer height data can be accessed at
The traffic count data used in this article were provided by
WSD made NO
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Will S. Drysdale acknowledges PhD studentship funding from the National Centre for Atmospheric Science (award ref: ncasstu002). The authors also acknowledge the National Centre for Atmospheric Science's support of the BT Tower observatory through the Atmospheric Measurement and Observation Facility. The National Ecological Observatory Network is a programme sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the National Science Foundation through the NEON Program. Additional thanks to Neil Mullinger for supporting the measurement infrastructure at the BT Tower. The authors would like to thank operational staff at the BT Tower for supporting this research.
This research has been supported by the UK Natural Environment Research Council and the Integrated Research Observation System for Clean Air project (grant no. NE/T001917/1) as well as through UKCEH's UK-SCAPE programme delivering National Capability (grant no. NE/R016429/1).
This paper was edited by Steven Brown and reviewed by three anonymous referees.