Introduction
Poor air quality has long been recognized as having adverse effects on
health. Particulate pollution in the UK has been assessed as causing a loss
of life expectancy from birth of approximately 6 months (COMEAP, 2010),
while air pollution in the WHO European Region was estimated to cause
600 000 premature deaths in 2010 (WHO, 2015). Improved understanding of
these health effects requires additional information about air quality,
especially in urban areas where high pollutant concentrations coincide with
high population densities.
Continuous air quality measurements, for example from the UK Automatic Urban
and Rural Network (AURN, Defra, 2017), are typically carried out at a limited
number of fixed locations in an urban area and are expected to be
representative of several square kilometres for urban background locations
(EC Directive, 2008). In addition, short-term intensive campaigns making use
of specialist monitoring equipment, as for example carried out for the
ClearfLo project (Clean Air for London; Bohnenstengel et al., 2015), are
of great value for detailed assessment of model performance and underlying
processes, whilst sampling equipment can also be carried by moving vehicles
or individuals for short-term detailed studies. In contrast to measurements,
air quality or atmospheric chemistry transport models, evaluated with the
above data, allow pollutant concentrations to be simulated with complete
spatial-temporal coverage leading to detailed calculations of population
exposure (Smith et al., 2016).
Air quality models require accurate input emissions data to make reliable
predictions of ambient concentrations. However, in the last decade, it has
become clear that measured NOx and NO2 concentrations have
not decreased as fast as would have been anticipated from published emission
factors (Carslaw et al., 2011). Several measurement techniques for direct
assessment of on-road tailpipe emissions, as reported by Carslaw and
Rhys-Taylor (2013) and O'Driscoll et al. (2016), have confirmed differences
from the official emissions estimates (EFT, Defra, 2016). In-service
emissions performance evaluation of Euro 6/VI vehicles (Moody and Tate, 2017)
indicated that, whilst Euro VI heavy-duty vehicles and Euro 6 petrol light-duty
vehicles are performing broadly as predicted, Euro 6 diesel light-duty
vehicles emit NOx at rates exceeding the published data, by factors
of up to 4.5.
There is further uncertainty in the rates of particulate emissions from road
vehicles due to wear of tyre, brake, and road surfaces and resuspension of
pre-existing particulates (Thorpe and Harrison, 2008). Particulate exhaust
emissions have decreased considerably in recent years, primarily due to the
introduction of diesel particulate filters, so the relative contribution of
non-exhaust PM10 and PM2.5 to total traffic emissions is now
considerable, of the order of half of the total exhaust (Grigoratos and
Martini, 2014).
Atmospheric chemistry models that simulate air quality vary in complexity in
terms of the scales and processes represented. Global and regional models use
gridded emissions data to calculate transport and chemistry over global or
regional modelling domains, such as EMEP MSC-W (EMEP, Simpson et al., 2012)
used in this study, CAMx (Ramboll Environment and Health, 2016), CMAQ (Byun
and Schere, 2006) and WRF-Chem (Grell et al., 2005). Models on a smaller
scale apply detailed transport and fast chemistry processes to individual
sources, such as ADMS-Urban (Owen et al., 2000) also used in this study, the
US EPA model AERMOD (Cimorelli et al., 2004) and CAR-FMI (Kukkonen et al.,
2001).
Global and regional air quality models typically use detailed chemistry
schemes whilst urban models typically only represent fast chemistry, such as
O3–NOx chemistry which is relevant for pollution
concentration gradients across urban areas. Some hybrid process–statistic-based
approaches have also been developed, where measured concentration data
are used to constrain modelled concentrations in order to reduce
uncertainties, for example those described in Stedman et al. (2001) and Sokhi
et al. (2008).
Urban dispersion models typically use measured upwind rural concentrations to
represent long-range transport. This is a successful approach for modelling
historic periods but has limited applicability for assessing future
scenarios, including those related to climate change or the local effects of
regional emissions changes. The use of a limited number of upwind monitoring
sites can also make the upwind concentration data less representative
and does not allow for variations over a large urban area. An alternative
method (Stocker et al., 2012, 2014) is to combine regional modelling with
urban local modelling in order to take into account both short-range and
long-range transport and chemistry effects, whilst avoiding
double counting of the gridded and explicit emissions. The balance between
regional and local influences differs according to the pollutant lifetime.
For example, concentrations of ozone and particulates, which have lifetimes
at the surface of days to weeks, are strongly influenced by regional
emissions and transport, whereas concentrations of NO2, with a
surface lifetime of around 1 day, are primarily related to the dispersion and
chemical transformation of local emissions.
The overall methodology for the detailed evaluation of air pollution
concentrations across London for 2012 using a coupled regional–urban model is
described in Sect. 2 with details of the measurement data, models, emissions
and statistical parameters. Section 3 gives the results of the model
evaluation against measured concentrations while Sect. 4 discusses the
results in relation to air quality in London and the different modelling
methods.
Number of monitoring sites per pollutant and site type, with average
heights over all sites and by site type. “Background” includes both
suburban and urban background sites while “near-road” includes sites
classified as roadside and kerbside. Note that all the selected O3
monitoring sites have co-located NO2 and NOx measurements.
Pollutant
Number of sites
Average site inlet height (m)
Total
Background
Near-road
All sites
Background
Near-road
NOx
42
15
27
3.0
3.3
2.8
NO2
42
15
27
3.0
3.3
2.8
O3
20
11
9
3.3
3.6
2.8
PM10
33
12
21
3.1
3.5
2.9
PM2.5
11
5
6
3.1
3.3
2.9
CO
7
3
4
2.8
3.0
2.6
Methods
This paper presents a detailed evaluation of air pollution concentrations
across London for 2012 using a coupled regional–urban model (described in
Sect. 2.4), which comprises a regional version of the EMEP atmospheric
chemistry transport model, EMEP4UK (Sect. 2.2), and the ADMS-Urban local
dispersion and chemistry model (Sect. 2.3). The coupled model is evaluated
alongside the stand-alone implementations of the two underlying models. The
evaluation exercise compares hourly modelled concentrations of NOx,
NO2, O3, CO, PM10 and PM2.5 with measured hourly
concentrations from up to 42 automatic monitoring sites within Greater London
for the year 2012, described in Sect. 2.1. The model simulations use road
vehicle emissions of NOx, NO2 and particulates which have
been adjusted in line with real-world emissions measurements. The emissions
data, including the raw 2012 emissions, adjustments and time variation, are
described in Sect. 2.5. Definitions of the statistics used for model
evaluation are given in Sect. 2.6.
Measurement data
The monitoring sites selected for the model evaluation were those from the
London Air Quality Network (LAQN, Mittal et al., 2017) located within Greater
London that had at least 70 % data capture of hourly data during 2012 for
PM10, PM2.5, CO or at least two of NOx, NO2 and
O3. A summary of site numbers by type and their average heights is
given in Table 1; with the exception of two background monitors at 5.5 and
10 m all monitors are between 2 and 4 m above ground level
(a.g.l.). The site locations of the NO2 and
O3 monitors are presented in Fig. 1. Note that all the map plots in
this paper adopt the polar stereographic coordinate system as used in
EMEP4UK, with an approximately 30∘ anticlockwise rotation of axes
compared to standard UK OSGB coordinates.
Locations of NO2 (black) and O3 (pale grey)
monitoring sites in Greater London, with square symbols for background sites
and round symbols for near-road sites. The London borough extents and
boundaries are shown for reference, alongside the extent of the locally
modelled emissions and the location of the measured meteorological data at
Heathrow airport.
Regional-scale modelling: EMEP4UK
EMEP4UK is a nested regional application of the EMEP MSC-W (European
Monitoring and Evaluation Programme Meteorological Synthesizing Centre-West)
model, focused specifically on air quality in the UK. The main EMEP MSC-W
model has been widely used for both scientific studies and for policymaking
in Europe, with references to evaluation and application studies available in
Simpson et al. (2012), Schulz et al. (2013) and at http://www.emep.int
(last access: 1 August 2018). EMEP4UK is described in Vieno et al. (2010,
2014, 2016a, b); the version used here is based on EMEP MSC-W
rv4.5. It uses one-way nesting from a
50 km × 50 km resolution greater European domain (standard EMEP
domain) to an inner 5 km × 5 km domain which covers the British
Isles and nearby parts of continental Europe, both in a polar stereographic
projected coordinate system, as shown in Fig. 2. An intermediate
10 km × 10 km resolution domain
is used for WRF (Weather Research and Forecasting Model) to ensure numerical
stability, but is not required for the chemistry transport calculations
(Vieno et al., 2010). Full details of the WRF model domains are given in
Table A1. The model has 21 vertical levels extending from the surface to
100 hPa, with the lowest vertical layer 50 m thick, meaning that modelled
surface concentrations represent a height of around 25 m. This is a finer
vertical resolution than was available in the standard EMEP model rv4.5.
Hourly average output concentrations are available from each cell for the
full 2012 modelling period.
The gaseous chemical scheme used in EMEP4UK in this study is the CRI-v2-R5
mechanism (Watson et al., 2008), which has 220 species and 609 reactions.
Five classes of fine and coarse particles, with differing size and deposition
properties, are used in EMEP4UK (Simpson et al., 2012) along with the
MARS (Model for an Aerosol Reacting System) equilibrium module for gas–aerosol partitioning of secondary inorganic
aerosol (Binkowski and Shankar, 1995; Simpson et al., 2012) and a treatment
of secondary organic aerosol formation using the volatility basis set
approach (Bergström et al., 2012; Ots et al., 2016). Dry (including
stomatal) and wet deposition of gases and particles are simulated. Sixteen
land cover classes are used for dry deposition modelling and for the
calculation of biogenic emissions. Ozone boundary conditions for the outer
European domain are based on the approach in Simpson et al. (2012), scaling a
monthly climatology with clean air measurements at Mace Head. Initial and
boundary conditions of all other species for the European domain are
specified as fixed functions of latitude and time of year.
The nesting structure used by the EMEP4UK model: an inner UK domain
simulated at 5 km × 5 km resolution within an outer European
domain simulated at 50 km × 50 km resolution, coloured by
anthropogenic NOx emission rates. The Greater London region, where
EMEP4UK supplies data to the coupled ADMS-Urban Regional Model Link (RML) system, is indicated by
white shading on the main figure and is shown inset on a larger scale.
The chemistry transport model was driven by meteorological output from the
WRF version 3.6.1 (Skamarock et al., 2008; NCAR, 2008) including data
assimilation of 6-hourly meteorology from the European Centre for Medium
Range Weather Forecasting ERA-Interim reanalysis (Dee et al., 2011). The
option of input meteorological data from WRF has been developed for EMEP4UK
and is used in both the European and UK domains. The WRF configuration was as
follows: Purdue Lin for
microphysics, Grell-3 for cumulus parameterization,
Goddard shortwave for radiation physics and
Yonsei University (YSU) for planetary
boundary layer (PBL) height. Land use categories were based on the MODIS IGBP
classification. This WRF configuration is similar to that discussed in Vieno
et al. (2010), where it is shown to perform well in comparison with
measurements. An evaluation of the WRF-EMEP4UK modelling system against
measured gaseous and particulate pollutant concentrations across the UK for
2001 to 2010 is given by Lin et al. (2017), while Ots et al. (2016) compares
WRF-EMEP4UK air quality simulations with detailed measurements of secondary
organic aerosols made in London during the 2012 ClearfLo campaign.
Urban-scale modelling: ADMS-Urban
The Atmospheric Dispersion Modelling System (ADMS, Carruthers et al., 1994)
is a quasi-Gaussian plume air dispersion model able to simulate a wide range
of passive and buoyant releases to the atmosphere. The dispersion
calculations are driven by hourly meteorological profiles of wind speed and
direction, among other parameters, which are characterized using
Monin–Obukhov length similarity theory; meteorological input data may be
derived from measurements or output from a mesoscale model such as WRF. ADMS
is a local dispersion model, which is able to resolve concentration gradients that
occur in the vicinity of a range of emission source types, including point,
jet, line, area and volume sources. The modelling of dispersion and chemistry
for source emissions is independent of the output grid resolution.
The ADMS-Urban model has been used to simulate air quality within cities
worldwide; applications include testing of emission-reduction scenarios and
forecasting (Stidworthy et al., 2017). Emissions from all sources within the
model domain are included, either explicitly with detailed time-varying
profiles, for instance major road and industrial sources, or as
grid-averaged emissions, representing diffuse sources such as those from
heating and minor roads as a grid of regular volume sources, with simpler
time variation.
The flow field that drives dispersion of pollutants within an urban area is
inhomogeneous. On the neighbourhood scale, buildings displace the upwind wind
speed profile and reduce in-canopy wind speeds. ADMS-Urban has an “urban
canopy” flow field module, which calculates wind speed and turbulence flow
profiles that relate to the spatial variation of the surface roughness
length, z0. Locally, if street canyons are formed by densely packed tall
buildings, it is important to model the complex combination of recirculating
and channelled flows. The ADMS-Urban advanced street canyon module is able to model (i) the
channelling of flow along and circulation of flow across a street canyon, (ii) asymmetric
street canyons, (iii) the effect of pavements within a canyon, and (iv) the
effect of a street canyon on the surrounding area. The module
has been validated extensively by comparison with measurements from
monitoring networks in Hong Kong and London (Hood et al., 2014). In the
present modelling the advanced street canyon option has been used for all
roads in the modelling domain with adjacent buildings.
For this project 3-D building data and road centreline locations from
Ordnance Survey MasterMap (Ordnance Survey, 2014) were processed for use in
ADMS-Urban, as described in Jackson et al. (2016), although using the EMEP4UK
polar stereographic projected coordinate system. The inputs to ADMS-Urban
take two forms: (i) gridded building height and density parameters for urban
canopy flow field calculations and (ii) street canyon properties for each side of
explicitly modelled road sources. Figure 3 shows the variation of average
building height over the Greater London area, which is used to determine the
local roughness length for flow calculations.
Spatial variation of the average building height (1 km × 1 km grid
cells) for Greater London.
In this study ADMS-Urban version 4.0.4 was used for the stand-alone runs,
with emissions covering the Greater London area, defined by the London Atmospheric Emissions Inventory
(LAEI) emissions extent. The stand-alone runs use hourly measured meteorological
data from Heathrow airport (location shown in Fig. 1) for the whole domain,
with valid data available for over 95 % of hours in 2012. Long-range
transport of NOx, NO2, O3, PM10, PM2.5
and SO2 is represented by hourly measured background concentrations from
rural sites upwind of London in the AURN monitoring
network (Ricardo-AEA, 2013): Wicken Fen (north of London, gaseous
pollutants only); Lullington Heath (south, gaseous pollutants only); Harwell
(west, all pollutants); and Rochester (east, all pollutants), providing
boundary conditions to the local modelling. No monitoring data are available
for CO, so a constant background concentration for CO of
220 µg m-3 was obtained from the annual mean background map
published by Defra (2013b). Output concentrations are given at hourly
resolution and post-processed to calculate long-term averages as required.
The modelling also uses the ADMS-Urban NOx photolytic chemistry
module, which accounts for fast, near-road oxidation of NO by O3 to
form NO2 (Smith et al., 2017), and simple sulphate chemistry for
conversion of SO2 to PM2.5 and PM10. The options for flow over
complex terrain (Carruthers et al., 2011), and gaseous and particulate wet
and dry deposition, are not used in this study.
Coupled regional-to-urban-scale model
At short times after release of a pollutant from a source, concentration
gradients due to releases from that source are high and a street-scale
resolution model such as ADMS-Urban is needed to capture the fine details of
dispersion and fast chemistry, for instance at roadside locations. At longer
times after release, pollutant concentration gradients are reduced by mixing
and a gridded regional model that accounts for long-range transport and
detailed chemical transformations simulates these processes adequately.
These models may be coupled within a single system. However, the
computational linkage process is non-trivial in order to avoid
double counting of emissions and to ensure that the chemical processes are
accounted for at all timescales.
The underlying concept for coupling the regional- and urban-scale models,
described in Stocker et al. (2012), is to use the local urban model to
represent the initial dispersion of emissions up to a mixing time, typically
1 h, after which the emissions are considered well mixed on the scale of the
regional model grid. In general, Gaussian plume models such as ADMS-Urban
treat plumes as continuing for all time, but within the coupled system the
calculations are truncated at the mixing time Δt. An ADMS-Urban run
with gridded emissions, limited to the mixing time (Cgrid(Δt)), is used to represent the regional model calculations within the mixing
time and is subtracted from the regional model output, CRM, in
order to avoid double counting emissions. The final concentrations from the
coupled model, Ccoupled, are then calculated by adding the output
from an ADMS-Urban run with explicit emissions, also limited to the mixing
time (Cexpl(Δt)), with the overall expression given as
Ccoupled=CRM-Cgrid(Δt)+Cexpl(Δt).
Additional steps calculate local background concentrations from the regional
model which are used as input to the subsequent ADMS-Urban runs to ensure
that the long-range transport and chemical environment is adequately
represented for local chemical processes.
The initial implementation of an automated coupled system using ADMS-Urban
and the CAMx regional model for Hong Kong is described in Stocker et
al. (2014), with evaluation against monitoring data. For the work described
in this paper, the coupled ADMS-Urban Regional Model Link (RML) system was
further developed to allow the ADMS-Urban runs to be carried out using the
ARCHER UK National Supercomputing Service. In the coupled system, meteorology
and background concentrations are extracted and used as inputs for separate
ADMS-Urban runs for each 5 km × 5 km EMEP4UK
grid cell, leading to spatially varying meteorology and background
concentrations across the modelling domain.
The version of ADMS-Urban (3.4.6) used within the coupled system was
slightly older than for the stand-alone runs (4.0.4); the older version was
modified for compatibility with the ARCHER supercomputer. There are no
relevant differences in terms of dispersion or chemical modelling between
ADMS-Urban versions 3.4.6 and 4.0.4.
Emissions data
2012 emissions
EMEP4UK uses anthropogenic emissions of NOx, NH3, SO2,
primary PM2.5, primary coarse PM (PM2.5–10), CO and
non-methane VOCs (volatile organic compounds). Emissions from the UK are
derived from the National Atmospheric Emission Inventory (NAEI, Tsagatakis et
al., 2016) for 2012 at 1 km resolution and aggregated to the model's
5 km × 5 km grid. Within Greater London these NAEI emissions are
replaced by the emissions prepared for ADMS-Urban as described below. Outside
the UK, EMEP4UK uses 2012 anthropogenic emissions provided by the EMEP Centre
for Emission Inventories and Projections (CEIP, www.ceip.at/, last
access: 1 August 2018) at 50 km resolution. Shipping emission estimates for
seas around the UK are derived from ENTEC (2010), projected to 2012. The
anthropogenic emissions are distributed vertically within the model according
to their Selected Nomenclature for sources of Air Pollution (SNAP) sector; for
example road transport emissions (sector 7) are assigned to the lowest layer
while power station emissions (sector 1) are assigned to layers between 184
and 1106 m (Simpson et al., 2012, Supplement). Biogenic emissions of
isoprenes and monoterpene are calculated at each time step according to
insolation and surface temperature (Guenther et al., 1995). Emissions of
wind-driven sea salt and NOx from soils are also calculated
interactively as described by Simpson et al. (2012), whereas lightning
NOx emissions are prescribed. Import of Saharan dust is treated
using a monthly dust climatology as a model boundary condition. Resuspension
of settled dust by wind is not included.
For ADMS-Urban the emissions for all sources except road traffic have
been taken from the London Atmospheric Emissions Inventory 2010 (GLA, 2013)
for the LAEI domain, which covers the area bounded by the M25 orbital
motorway. The emissions have been projected from the LAEI base year 2010 to
the modelling year 2012. Road traffic emissions have been calculated using
activity data from the LAEI. The emission factors used to calculate emission
rates are based on the UK NAEI 2014, which includes speed-emissions data from
the COPERT 4 version 10 software tool (Katsis et al., 2012). However, due to
uncertainties in NOx emissions factors for some diesel vehicles and
non-exhaust particulate emission factors, adjustments have been made to the
published factors to improve consistency with real-world emissions
measurements. The adjustments are discussed further in Sect. 2.5.2 and their
effects on the modelled concentrations examined in Sect. 3.1.
The NAEI and LAEI emissions are supplied as a regular, orthogonal 1 km
resolution grid in the OSGB coordinate system. The use of the EMEP4UK model
in this study requires a conversion to the polar stereographic coordinate
system, with reaggregation onto a grid with a different orientation. This
causes some loss of precision in the location of emissions, which is more
acute for the ADMS-Urban runs with 1 km gridded emissions than for the
EMEP4UK runs with 5 km grid resolution. The average 1 km gridded emissions
are reduced by around 5 % as a result of the re-gridding process. Within
the coupled modelling system, this reduction of average emissions makes
little difference as it only affects the ADMS-Urban run including explicit
sources, where concentrations are dominated by the unaffected explicit
emissions due to running a limited spatial extent. The change is also small
relative to the real-world adjustments and other sources of uncertainty
in the emissions. For consistency, the stand-alone local model runs have used
the same coordinate system as EMEP4UK and the coupled system in this study.
In ADMS-Urban the road source emissions are modelled with a standard initial
mixing height of 2 m a.g.l., although they may be distributed further
upwards due to street canyon effects. Aggregated emissions, represented as
gridded volume sources, have a depth of 100 m in the runs without explicit
sources, in order to match the behaviour of the EMEP4UK modelling, and a
depth of 10 m in the run with explicit sources, where individual point
sources are modelled with release heights of 30–200 m.
Road traffic emissions factor adjustments
A significant cause of the discrepancies in NOx and NO2
emission rates between published figures and real-world measurements is the
difference in driving conditions between standard test cycles and real
journeys, especially those in congested urban traffic (Franco et al., 2013).
This issue was highlighted in 2015 when it became apparent that Volkswagen
had installed software in their diesel cars that automatically reconfigured
the engine during emissions testing (Oldenkamp et al., 2016). The
discrepancies in European vehicle emission rates are expected to begin to
decrease due to recent legislative changes (Commission Regulation (EU)
2016/646) which require the use of urban driving cycles and real-world
assessment for emissions testing. Emission factor adjustments are still
likely to be necessary for modelling older vehicles which will remain in the
active fleet.
Measured volume ratios of NOx and NO2 to CO2 emissions
(a proxy for fuel usage) have been compiled for a range of vehicles,
categorized by Euro emission standard and size, with corresponding speeds by
Carslaw and Rhys-Tyler (2013). Measurements were taken at four sites,
representing roads in central and outer London. Additional data from bus
monitoring campaigns are provided in Carslaw and Priestman (2015) and used
for buses running with compressed natural gas fuel. For this study, to make
use of these measured data to improve road traffic emissions, the emissions
inventory toolkit (EMIT, CERC, 2015) software was used to calculate standard
ratios of NOx to CO2 emissions from the raw NAEI data set for
different vehicle types and Euro classes, for average speeds as available in
the measured data. A total of 22 vehicle categories were used for light vehicles and 17 categories for heavy
vehicles, with scaling factors calculated from the measured data ranging from
0.80 for Euro II buses to 3.32 for Euro IV buses with selective catalytic
reduction (SCR). These scaling factors were used to recalculate NOx
emission rates. This methodology assumes that the standard CO2 emissions
factors are substantially more accurate than the NOx factors,
although the former also contain uncertainties. Diesel cars, which make up
41 % of the London car fleet (excluding taxis) for 2012, are calculated
to have fleet-weighted emissions of NOx 31 % higher due to the
adjustment. Over all road traffic sources in London, the adjustments to
emission factors caused an increase in total annual NOx emissions
of 55 %. The standard primary fraction of NOx emitted as
NO2 is retained for each vehicle class, but as the NOx
emissions adjustment varies between vehicle classes, the total NO2
emissions do not increase by the same proportion as the NOx
emissions.
Estimates of emission factors used to represent non-exhaust particulate
components are relatively unrefined, for example the EMEP CORINAIR
non-exhaust factors use a linear speed-emissions profile and a maximum of
10 vehicle categories, in contrast to the hundreds of vehicle categories
used for exhaust emissions classification. Analyses of roadside measurements
demonstrate that the contribution from brake wear in particular is
considerably higher than the published factors (GLA, 2016).
Non-exhaust particulate emission factors were adjusted based on work by
Harrison et al. (2012), who analysed measurements of speciated and
size-segregated particulates at the Marylebone Road monitoring site and
nearby urban background sites, made during four month-long
campaigns between 2007 and 2011. Non-exhaust emissions were found to
contribute 77 % of the total traffic-related particulate emissions, with
55 % of the non-exhaust attributable to brake wear and smaller
proportions from resuspension of road dust and tyre wear. Assuming that the
standard exhaust emission factors are reliable, the non-exhaust emission
factors were scaled in EMIT in order to make up 77 % of the total traffic
emissions and to have the correct proportionality between the different
components. This is consistent with the approach taken in the LAEI 2013 (GLA,
2016). Applying these adjustments increases the total annual PM10
emissions from road traffic sources by 45 %. Basing the adjustment of all
road non-exhaust emissions on measurements from one site is an approximation,
but it is still expected to improve the overall estimates of non-exhaust
emissions due to the substantial uncertainty in the standard factors.
London 2012 emissions inventory, with source counts given in
brackets in the key. Note that railway and river shipping sources are
represented by road sources with altered source properties. The gridded
sources are 1 km × 1 km in extent.
Total emission rates within the LAEI area by SNAP sector, including
the effects of real-world emission adjustments for NOx and PM from
road transport (sector 7). Italic font indicates the road transport and total
emissions with unadjusted (raw) emission factors.
SNAP sector
Description
Total emission rate (Mg yr-1)
NOx
NO2
PM10
PM2.5
CO
1
Energy production
7886
394
307
0
918
2
Domestic and commercial combustion
3887
194
99
60
5428
3
Industrial combustion
4796
240
192
115
3367
4
Production processes
948
47
227
22
435
5
Fossil fuel extraction and distribution
0
0
0
0
0
6
Solvent use
0
0
28
0
0
7
Road transport (raw)
32 147
8520
2724
1420
48 738
7
Road transport (adjusted)
49 673
11 878
3943
1916
48 738
8
Other transport
8439
587
197
150
29 173
9
Waste treatment and disposal
1647
82
168
150
508
10
Agriculture
0
0
15
1
0
11
Nature
96
5
106
76
0
Total with raw road transport
59 845
10 070
4063
1996
88 568
Total with adjusted road transport
77 371
13 429
5282
2491
88 568
Change in total due to adjustments (%)
29
33
30
25
0
The adjusted emissions that reflect real-world conditions as well as possible
are hereafter referred to as real-world emissions. The total emissions
for the LAEI area are summarized by sector in Table 2, including the effects
of the adjustments to road transport emissions. Note that CO emissions are
unaffected by the road traffic adjustments. A graphical representation of the
emissions used in ADMS-Urban is shown in Fig. 4.
Time variation of emissions
In addition to annual average emission rates, it is important for models to
capture the temporal variation of emissions in order to represent the
short-term variation of concentrations. Within EMEP4UK, the 2012 annual
total anthropogenic emissions derived from the inventories are resolved to
hourly resolution using prescribed monthly, day-of-week and diurnal hourly
emissions factors (the latter differing between weekdays, Saturdays and
Sundays) for each pollutant and for each of the SNAP sectors (Simpson et
al., 2012).
The stand-alone local model implementation uses an hourly time-varying
profile for weekdays, Saturdays and Sundays for all explicit road sources and
for aggregated emissions. This time-varying profile is based on a long-term
analysis of NOx measurements in central London (Beevers et al.,
2009). The ADMS-Urban runs with gridded emissions within the coupled system
use a simplified version of the EMEP4UK monthly and hourly time-varying
profiles for NOx and PM, combined using a weighting by total
emissions for each sector, while runs with explicit emissions use the same
profile as in the stand-alone implementation for explicit road sources.
Model evaluation statistics
The following statistics are used to evaluate the modelled concentrations M
in relation to the observed concentrations O; n is the number of pairs of
modelled and observed concentrations, a bar indicates the mean value (e.g.
M‾) and a subscript indicates a single parameter value ranked
between unity and n (e.g. Mi).
Fractional bias (Fb) is a measure of the mean difference between the modelled
and observed concentrations:
Fb=M‾-O‾0.5O‾+M‾.
Normalized mean square error (NMSE) is a measure of the mean difference
between matched pairs of modelled and observed concentrations:
NMSE=M-O2‾MO‾.
Pearson's correlation coefficient (R) is a measure of the extent of a
linear relationship between the modelled and observed concentrations:
R=1n-1∑i=1nMi-M‾σMOi-O‾σO,
where σO is the standard deviation of observed concentrations and
σM is the standard deviation of modelled concentrations.
Model evaluation statistics calculated from hourly average modelled
and monitored concentrations for stand-alone ADMS-Urban runs with raw (r) and
adjusted (a) road traffic NOx and PM emissions, and the % change
in concentrations due to the emissions adjustment by site type Bgd
(background) or Nr-Rd (near-road). Fb – fractional bias in annual average
concentration, ideal value 0.0; NMSE – normalized mean square error in
hourly concentrations, ideal value 0.0; R – correlation coefficient for
hourly concentrations, ideal value 1.0.
Annual mean
Conc.
concentration (µg m-3)
change
Model evaluation statistics
Poll
Site type
Sites
Obs
Mod
Mod
Mod %
Fb
Fb
NMSE
NMSE
R
R
(r)
(a)
((a - r) / r)
(r)
(a)
(r)
(a)
(r)
(a)
NOx
Bgd
15
58.7
49.9
61.6
23.4
0.16
0.05
0.93
0.86
0.62
0.61
NOx
Nr-Rd
27
149.6
105.9
149.6
41.3
-0.34
0.00
0.88
0.62
0.63
0.62
NO2
Bgd
15
35.4
30.4
36.0
18.4
-0.15
0.02
0.32
0.27
0.66
0.67
NO2
Nr-Rd
27
60.8
51.0
64.7
26.9
-0.18
0.06
0.32
0.28
0.64
0.64
O3
Bgd
11
35.5
37.1
35.0
-5.7
0.04
-0.01
0.21
0.21
0.76
0.77
O3
Nr-Rd
9
26.9
31.7
27.5
-13.2
0.16
0.02
0.30
0.29
0.75
0.77
PM10
Bgd
12
19.0
18.6
19.4
4.3
-0.02
0.02
0.27
0.27
0.69
0.69
PM10
Nr-Rd
21
27.1
22.1
25.4
14.9
-0.20
-0.07
0.45
0.37
0.58
0.58
PM2.5
Bgd
5
13.7
14.1
14.4
2.1
0.03
0.05
0.29
0.29
0.76
0.77
PM2.5
Nr-Rd
6
15.7
16.0
17.2
7.5
0.02
0.09
0.30
0.30
0.73
0.72
CO
Bgd
3
261.2
273.6
273.6
0.0
0.05
0.05
0.26
0.26
0.40
0.40
CO
Nr-Rd
4
365.4
429.0
428.9
0.0
0.16
0.16
0.41
0.41
0.48
0.48
Fraction of modelled hourly concentrations within a factor of 2 of
observations (Fac2) is given by the fraction of model predictions that
satisfy
0.5≤MiOi≤2.0.
The model quality indicator (MQI, Thunis and Cuvelier, 2016) has been
developed through the Forum for Air quality Modelling in Europe (FAIRMODE,
Janssen et al., 2017) as an overall metric of model performance which depends
on the measurement uncertainty. The MQI is defined as the ratio between the
model bias and twice the measurement uncertainty (RMSU, scaled
from the estimated measurement uncertainty at the relevant limit value);
lower values reflect better model performance and values of the MQI less than
1 are considered to fulfil the modelling quality objective, in which case
model bias is less than twice the measurement uncertainty. This statistic is
not defined for NOx or CO as there are no EU limit values for
NOx, whilst CO is typically well below the EU limit value, so it is not
normally assessed. On the assessment target plot, the MQI represents the
distance between the origin and a given station point as follows.
MQI=RMSE2RMSU,
where
RMSE=1n∑i=1nOi-Mi2
and the ordinate and abscissa correspond to the bias M‾-O‾ and CRMSE (centred root mean square error):
CRMSE=1n∑i=1nMi-M‾-Oi-O‾2,
where both statistics are normalized by twice the measurement uncertainty.
The robust highest concentration (RHC) gives an indication of the performance
of the model for high hourly concentrations and is defined as
RHC=χ(j)+χ-χ(j)ln3j-12,
where j is the number of values considered as the upper end of the
concentration distribution, χ is the average of the j-1 largest
values and χ(j) is the jth largest value. The value of n is set to
26, as used in Perry et al. (2005).
Results
Section 3.1 assesses the impact of the emissions adjustments on simulated
concentrations using the stand-alone ADMS-Urban local model. Section 3.2
presents the spatial variation of annual average NO2, O3 and
PM2.5 concentrations across London predicted by the coupled modelling
system while Sect. 3.3 gives detailed evaluation statistics for the regional,
local and coupled models based on hourly concentration data for all modelled
species. Section 3.4 presents additional analysis of the annual average
modelled and measured concentrations while Sect. 3.5 concerns the hourly
average concentrations and diurnal cycles for NOx, NO2 and
O3. The regulatory standards for NO2, which are defined for
annual average and maximum hourly concentrations, have driven this study's
focus on these two averaging periods.
NO2 annual average concentrations from the coupled model for
the whole of Greater London (a) and an area of central
London (b), with monitoring data overlaid – round symbols for
near-road sites and square symbols for background sites.
O3 annual average concentration contours from the coupled
model for the whole of Greater London (a) and an area of central
London (b), with monitoring data overlaid – round symbols for
near-road sites and square symbols for background sites.
Impact of emission adjustments on modelled concentrations
The effect of the adjustment of road traffic NOx and PM emissions
to reflect real-world conditions on all simulated species is shown for
background and near-road site types across London in Table 3. This comparison
was performed as a preliminary assessment using simulations from the
stand-alone ADMS-Urban local model since for this model measured background
concentrations are utilized, leading to lower uncertainty in the long-range
transport component of concentrations in the stand-alone model than in the
coupled system, where the long-range transport contribution is also modelled.
The reduced uncertainty means that model errors are the most closely
associated with local emissions for this model. The statistics presented are
fractional bias, normalized mean square error and correlation coefficient.
The CO concentration results show negligible changes due to the adjustment of
emissions, as expected, since CO emissions were not changed. For
NOx, NO2, O3 and PM10 the emission adjustments
result in substantial concentration changes and improvements in Fb and NMSE,
especially for near-road sites. For NOx the concentrations are
increased, with Fb values reduced from around -0.3 to close to zero for
near-road sites and NMSE reduced substantially; there are smaller
concentration and statistics changes for NO2. The change in
NOx concentrations at background sites (+23 %) is similar to
the change in total emissions (+29 %, Table 2), reflecting the direct
link from emissions to concentrations for NOx. The change in
NO2 concentrations at background sites (+18 %) is smaller than
both the NO2 emissions change (33 %) and the NOx
concentration change, since emitted NO2 contributes only a relatively
small amount to total NO2 and due to the time required for chemical
processes to convert NO to NO2, which means the response of
NO2 concentrations to NOx emissions is less than linear.
For O3 the impact of the adjusted NOx and NO2
emissions leads to lower concentrations and reduces the Fb from 0.16 to 0.02
for near-road sites, although there is little change in the NMSE. For
PM10, concentrations are higher, so the magnitude of the negative Fb
values is smaller when using the adjusted emissions, whilst NMSE values are
lower over near-road sites but not background sites, which are dominated by
regional PM. The large relative contribution of regional PM also causes the
concentration changes (2 %–15 %) to be substantially smaller than the
emissions changes (25 %–30 %) for these pollutants. For PM2.5 the
small overestimate of concentrations is increased by the emissions
adjustment: Fb increases at near-road sites from 0.02 to 0.09. For all
species the correlation coefficients remain very similar when using adjusted
emission compared to the raw emissions, consistent with the correlation being
influenced mainly by the variation in the relative magnitude of
concentrations over time, not by their absolute magnitude.
All remaining model results presented in this section use the adjusted road
traffic emissions.
PM2.5 annual average concentration contours from the coupled
model for the whole of Greater London (a) and an area of central
London (b), with monitoring data overlaid – round symbols for
near-road sites and square symbols for background sites.
Spatial variation of NO2, O3 and PM across
London
Annual average contour plots of concentrations for NO2, O3 and
PM2.5 produced from the hourly coupled regional-to-urban model output
using the adjusted emissions data are shown in Figs. 5–7. The influence of
the M25 London orbital motorway is clearly visible for all three species. The
corresponding monitored data are overlaid as coloured points. For
NO2, the highest concentrations (over 100 µg m-3 in
central London) are found near busy roads, while away from roads the
concentrations increase from around 20 µg m-3 outside the M25
to around 50 µg m-3 in the centre of the urban area. The
average NO2 concentration calculated by the coupled model at urban
background monitoring sites is 36 µg m-3, just below the EU
annual average limit value of 40 µg m-3 for NO2,
while at near-road sites it is 60 µg m-3, substantially above
the limit value; corresponding measured values are 35 µg m-3
for background sites and 61 µg m-3 for near-road sites. The
modelled fraction of NOx which is NO2 increases from 0.43
at near-road sites to 0.59 at urban background sites, due to chemical
conversion of emitted NO to NO2. Across London, 333 km2
(13 %) of the 2690 km2 urban area within the M25 motorway,
excluding road carriageways, exceeds the NO2 annual average limit
value, as shown by the yellow, orange and red colours in Fig. 5.
Annual average O3 concentrations show an inverse pattern to
NO2, with low concentrations near busy roads (<25 µg m-3) and in the centre of the urban area, due to the
effects of titration of O3 by NO. There is no relevant limit value
for annual average O3 for comparison. PM2.5 concentrations show
more uniform background concentrations of less than 10 µg m-3
throughout the urban area, with steep increments near roads. The average
PM2.5 concentration calculated at urban background monitoring sites is
8.9 µg m-3 and at near-road sites is
11.2 µg m-3, both substantially below the annual average
limit value of 25 µg m-3 for PM2.5. The increase in
average concentrations at near-road sites over background sites is similar to
the corresponding measured value, although the overall values are
under-predicted (measured 13.7 µg m-3 at background sites and
15.7 µg m-3 at near-road sites, still well below the limit
values). The absolute and relative concentration increment at near-road sites
over urban background sites is smaller for PM2.5 than for NO2;
this difference is captured by the coupled modelling system. A negligible
fraction of the urban area (0.003 %) exceeds the annual average limit
value of 25 µg m-3 for PM2.5, as shown by the
predominantly blue and green colours in Fig. 7. A corresponding plot for
PM10 concentrations, showing very similar patterns to PM2.5 and
negligible exceedances of the annual average limit value of
40 µg m-3, is given in Fig. A1.
Plots of annual average concentration of NO2 and O3 against
site height, calculated from hourly observations and hourly coupled model
output for monitors where both NO2 and O3 are available, are
given in Fig. A2. They show generally good agreement between the modelled and
observed concentrations, with the increased NO2 and reduced
O3 at near-road sites compared to background sites captured by the
model. There is no clear relationship between the concentrations and the site
height, especially at the background sites where there is a slightly wider
range of site heights.
Overall, the modelled pollutant distributions are closely related to the
locations of explicit emissions sources and are also in good agreement with
the spatial variation of observed concentrations, especially when viewed at
street-scale resolution. The comparisons between modelled and monitored
concentrations are discussed in more detail in the following sections.
NOx and NO2 model evaluation statistics calculated
at 42 sites for regional (EMEP), local (ADMS-Urban), and coupled modelled and
measured hourly concentrations. Fb – fractional bias in annual average,
ideal value 0.0; NMSE – normalized mean square error in hourly
concentrations, ideal value 0.0; R – correlation coefficient of hourly
concentrations, ideal value 1.0; Fac2 – fraction of hourly modelled
concentrations within a factor of 2 of observed, ideal value 1.0; MQI –
model quality indicator (annual), target value ≤1.0; average RHC –
average over all sites of robust highest concentration calculated for each
site (hourly). Note that MQI is not defined for NOx.
Annual mean
Average RHC
Poll
Model
(µg m-3)
Model evaluation statistics
(µg m-3)
Obs
Mod
Fb
NMSE
R
Fac2
MQI
Obs
Mod
NOx
EMEP
117.3
50.7
-0.793
2.962
0.425
0.481
–
1111
585
NOx
ADMS-Urban
117.3
118.3
0.009
0.728
0.669
0.713
–
1111
887
NOx
Coupled
117.3
111.7
-0.053
0.735
0.670
0.722
–
1111
750
NO2
EMEP
51.8
32.7
-0.453
0.819
0.459
0.639
1.31
217
176
NO2
ADMS-Urban
51.8
54.5
0.051
0.293
0.688
0.829
0.93
217
228
NO2
Coupled
51.8
51.4
-0.007
0.302
0.674
0.828
0.94
217
204
O3 model evaluation statistics calculated at 20 sites, with
statistics for NOx and NO2 at the same sites, for regional
(EMEP), local (ADMS-Urban) and coupled modelled hourly concentrations.
Statistics as defined for Table 4. Note that MQI is not defined for
NOx.
Annual mean
Average RHC
Poll
Model
(µg m-3)
Model evaluation statistics
(µg m-3)
Obs
Mod
Fb
NMSE
R
Fac2
MQI
Obs
Mod
O3
EMEP
31.6
36.9
0.153
0.358
0.659
0.633
0.72
154
130
O3
ADMS-Urban
31.6
31.7
0.001
0.241
0.777
0.664
0.37
154
122
O3
Coupled
31.6
32.4
0.023
0.325
0.698
0.650
0.45
154
129
NOx
EMEP
106.1
52.5
-0.676
2.865
0.401
0.555
–
1058
572
NOx
ADMS-Urban
106.1
96.6
-0.094
0.787
0.709
0.728
–
1058
797
NOx
Coupled
106.1
97.3
-0.087
0.784
0.711
0.736
–
1058
684
NO2
EMEP
47.2
33.6
-0.337
0.608
0.510
0.695
1.17
206
172
NO2
ADMS-Urban
47.2
47.6
0.008
0.258
0.725
0.845
0.82
206
204
NO2
Coupled
47.2
47.4
0.004
0.262
0.721
0.845
0.88
206
191
Evaluation statistics for NO2, O3, CO, PM10
and PM2.5 for regional, local and coupled models
The performance of the regional EMEP4UK, local ADMS-Urban and coupled models
has been assessed using evaluation statistics calculated from hourly
concentrations of each pollutant. Table 4 gives statistics for NOx
and NO2 at all of the 42 background and near-road sites at which they
are measured, while Table 5 gives statistics for O3, NOx
and NO2 at the 20 sites where O3 is measured, in order to
allow detailed analysis of these closely related pollutants at a consistent
set of sites. Table 6 gives corresponding statistics for CO and for the
particulate pollutants PM10 and PM2.5. An additional visual
representation of model performance, plots of NMSE against fractional bias,
is given in Fig. A3. The statistics include those presented for the emissions
adjustments in Table 3 (Sect. 3.1) as well as the fraction of modelled
hourly concentrations within a factor of 2 of observations (Fac2) and the
model quality indicator. The observed and modelled values of robust
highest concentrations are also presented. If this value is calculated
from all observed or modelled data, it is likely to be dominated by the
highest values at a single site, so the approach of averaging individual site
values over all sites has been taken in order to calculate more
representative values for high observed and modelled concentrations.
The average measured NOx and NO2 concentrations are lower
for sites with O3 measurements (shown in Table 5) compared to all
sites (Table 4), as there is a lower proportion of near-road sites with
O3. However, the general findings are the same for both sets of
sites. The regional EMEP model underestimates NOx and NO2,
as expected for a model using 5 km × 5 km gridded emissions. The
stand-alone ADMS-Urban model and coupled ADMS-Urban RML system show broadly
similar performance for the gaseous pollutants, indicating that the regional
model is performing well at simulating the local background gaseous
concentrations. For NOx and NO2, the Fb and NMSE values are
much lower when simulated by the stand-alone and coupled models than for the
regional model, due to the dominant influence of local emissions in
determining concentrations for these short-lived species. Correlation
coefficients are higher, with values of around 0.68 for both species for the
stand-alone and coupled model simulations, as well as a similar increase in
Fac2. The MQI values for all models except EMEP are less than 1 for
NO2, indicating achievement of the FAIRMODE model quality objective.
The modelled RHC shows both stand-alone and coupled models have good
performance in the prediction of peak NO2 concentrations. However,
these models underestimate peak NOx values and have values of Fb
for NO2 greater than those for NOx, suggesting some
over-prediction of NO2 relative to NOx in general. This is
at least in part likely to be due to an overestimate of the assumed fractions
of NOx emitted as NO2 (Carslaw et al., 2016).
CO model evaluation statistics calculated at 7 sites and particulate
pollutants statistics calculated at 33 sites (PM10) and 11 sites
(PM2.5) from regional (EMEP), local (ADMS-Urban) and coupled modelled
hourly concentrations. Statistics as defined for Table 4. Note that MQI is
not defined for CO.
Annual mean
Average RHC
Poll
Model
(µg m-3)
Model evaluation statistics
(µg m-3)
Obs
Mod
Fb
NMSE
R
Fac2
MQI
Obs
Mod
CO
EMEP
318.8
232.8
-0.312
0.809
0.295
0.656
–
2059
1335
CO
ADMS-Urban
318.8
359.5
0.120
0.383
0.504
0.763
–
2059
1327
CO
Coupled
318.8
317.3
-0.005
0.442
0.527
0.783
–
2059
1517
PM10
EMEP
24.2
17.1
-0.341
0.789
0.393
0.670
0.96
205
103
PM10
ADMS-Urban
24.2
23.2
-0.041
0.353
0.621
0.882
0.55
205
121
PM10
Coupled
24.2
21.0
-0.139
0.530
0.472
0.792
0.80
205
110
PM2.5
EMEP
14.7
8.7
-0.511
0.949
0.648
0.561
0.73
110
80
PM2.5
ADMS-Urban
14.7
15.8
0.074
0.295
0.746
0.824
0.41
110
98
PM2.5
Coupled
14.7
10.0
-0.377
0.749
0.633
0.669
0.68
110
81
The Fb values for O3 concentrations from the urban and coupled models
are also low (0.001–0.02), whilst the NMSE, R values and Fac2 results are
fairly similar when comparing all three models to measurements. This reflects
the significant contribution of regional background O3 concentrations
to the local concentrations within the urban area. The lower values of MQI
for the urban and coupled models show improved overall model performance due
to the inclusion of explicit sources. All three modelled RHC values are lower
than the observations, indicating that, although the annual average O3
concentrations are overestimated, the highest hourly concentrations are
underestimated. This is likely to be due to additional short-term chemistry
effects, for instance those caused by large increases in biogenic emissions
in hot conditions (Guenther et al., 2006), which are not well captured by the
models.
Although no adjustment was applied to the emission rates for CO there is
reasonable agreement between model and observations, with particularly good
values of Fb and Fac2 for the coupled model. The EU air quality standard for
CO is 10 mg m-3 for maximum daily 8 h mean, whereas the maximum hourly
observed concentration is around 2 mg m-3, consistent with no observed
exceedances of this standard.
For the particulate pollutants the ADMS-Urban model with measured background
concentrations performs markedly better than the coupled model due to poorer
performance of the regional model for these pollutants than for the gaseous
species in predicting background concentrations. The Fb shows that the
regional model underestimates PM10 and PM2.5 compared to
measurements (-0.3 to -0.5); the coupled model also underestimates these
species' concentrations compared to measurements but to a lesser extent
(-0.1 to -0.4), whereas for the stand-alone model Fb is close to zero.
These results reflect the significant regional contribution to local
measurements of particulates. Correlation coefficients between modelled and
measured concentrations are also higher for the stand-alone model than the
other two models, but less so for PM2.5 than PM10. The modelled PM
RHC values for all three models are lower than the observed RHC values,
particularly for PM10. Very high PM10 concentrations are often
related to specific local events, such as dust from construction sites, which
are not captured by annual average emissions inventories such as the LAEI
(Fuller and Green, 2004).
Scatter plot comparing annual average fractional bias for
NO2 and O3 for each of the 20 sites where O3 is
measured, for each model. The dotted line represents a fractional bias of
15 %, which is the required maximum measurement uncertainty under
directive 2008/50/EC (EC Directive, 2008).
Model assessment target plots for PM10 (a) and
PM2.5 (b) for coupled system outputs. Each symbol represents a
single station and the distance between the origin and the symbol corresponds
to the MQI for that station; the ordinate and abscissa correspond to the bias
and CRMSE respectively. Good model performance is indicated by points within
the green shading.
Annual average concentrations for NO2, O3 and
PM2.5
Some pollutants are closely connected by chemical or physical processes. Here
the model performance for NO2 and O3 concentrations is
evaluated concurrently. Figure 8 compares the annual average fractional bias
for NO2 and O3 for each model for background and near-road
site locations. For many sites the fractional bias of modelled concentrations
for both pollutants from each model is within an estimated measurement
uncertainty of 15 %, as shown on the plot by the square of dotted lines.
This is the maximum uncertainty allowable in continuous measurements reported
to the EU (EC Directive, 2008). The remaining points, especially those for
the regional model at near-road sites, most commonly show that overestimates
of O3 are associated with underestimates of NO2 while
underestimates of O3 are associated with overestimates of
NO2, as expected from the fast O3 titration chemistry that
usually prevails in the urban high-NOx environment. There are two
near-road sites where the coupled model overestimates both NO2 and
O3, which does not fit the general pattern.
Assessment target plots (developed as part of the DELTA tool within FAIRMODE,
Janssen et al., 2017) allow model performance to be evaluated with an
allowance for the measurement uncertainty (Pernigotti et al., 2013), which is
particularly relevant for particulate pollutants because of their higher
measurement uncertainty compared to gaseous pollutants. Figure 9 shows the
coupled model results for PM10 and PM2.5 presented on target plots
showing the normalized bias against the centred root mean square error
(CRMSE) for each monitoring site, such that the distance of points from the
origin gives the value of the MQI and allowance is made for measurement
uncertainty. Equivalent plots for O3 and NO2 are given in
Fig. A4. The quadrant in which the points are located depends on the
magnitude of the relative contributions of any lack of correlation and
standard deviation to the model error. Note that the correlation here is
calculated with a consideration of measurement uncertainty and is different
from the values given in Table 6. The area of the plot with green shading
shows where the model errors are within a factor of 2 of the measurement
uncertainty, leading to a value of MQI below 1. All of the PM2.5 sites
and all except one of the PM10 sites
lie within the green shading, which indicates achievement of FAIRMODE's model
quality objective. The plots show that the errors that occur are mainly
associated with negative bias (underestimate), as noted for Fb values in
Table 6 (Sect. 3.3), and lack of correlation.
Frequency scatter plots for each model and site type showing the
distributions of hourly average modelled and observed NOx
concentrations (for sites where O3 is also measured), where the
colour represents the density of points for a given combination of measured
and modelled values.
Frequency scatter plots for each model and site type showing the
distributions of hourly average modelled and observed NO2
concentrations (for sites where O3 is also measured), where the
colour represents the density of points for a given combination of measured
and modelled values.
Hourly concentrations and diurnal cycles for NO2 and
O3
In this section an evaluation of hourly data and of diurnal cycles is
performed. Figures 10–12 present frequency scatter plots of hourly
NOx, NO2 and O3 concentrations respectively, over
all the sites where O3 is measured, split by model and site type. The
NOx plots show a large spread for high concentrations from the
regional model at background sites, which is reflected in the coupled model.
This may indicate an inaccuracy in the diurnal variation of emissions used in
the regional model. The stand-alone local and coupled models capture the
large range of observed hourly concentrations; however, as expected and noted
from the evaluation statistics, the regional model underestimates
NOx at near-road sites.
The scatter of NO2 concentrations is substantially smaller than that
for NOx concentrations, since the dependence of NO2 on
NOx is less than linear on account of the proportion of
NOx that is NO2 increasing with distance from an emission
source, due to the chemical reaction of NO with O3, whilst the
concentration of NOx decreases due to mixing and dilution. A high
density of points (indicated by the red and orange colours) lying close to
the y=x line indicates that a model accurately captures the complex balance
between chemical and dispersion processes; Fig. 11 shows that the local and
coupled models perform well at both background and roadside sites, but the
regional model is unable to represent near-road NO2. For a small
number of hours NO2 is over-predicted by the local and coupled
models; this may be due to some overestimate of the fraction of
NOx, which is emitted in the form of NO2 (primary NO2),
and to limitations in the local chemistry scheme in these cases. The plots
for O3 (Fig. 12) show generally good agreement between modelled and
observed hourly concentrations for all models at all sites but they show an
under-prediction of the peak observed values. Some of this may relate to the
corresponding over-prediction of NO2, suggesting that the rate of
local O3 production through NO2 photolysis is
underestimated. The under-prediction of peak background O3
concentrations by the regional model is reflected in the coupled model
results. The under-prediction of peak urban background concentrations by the
stand-alone local model using measured rural upwind O3 also indicates
an underestimate of the local generation of O3 through
photochemistry within the urban area, for example due to an underestimate of
biogenic VOCs in hot conditions (Malkin et al., 2016).
Frequency scatter plots for each model and site type showing the
distributions of hourly average modelled and observed O3
concentrations, where the colour represents the density of points for a given
combination of measured and modelled values.
Diurnal temporal variations of NOx, NO2 and
O3 concentrations for the average over all background and near-road
sites, and for an individual near-road site, with observations and modelled
concentrations from each model. Note different concentration axis limits for
each plot. The shaded area around the central line shows the 95 %
confidence interval in the mean.
Mean diurnal profiles and 95 % confidence intervals for NOx,
NO2 and O3 averaged over background and near-road sites are
shown in Fig. 13, alongside a specific near-road site (BT4) in order to
demonstrate the variability in individual sites. The BT4 site is located
alongside the inner orbital North Circular Road, with annual average daily
traffic of 108 000 vehicles spread across 6 lanes of traffic, and a
neighbouring car park. The stand-alone urban and coupled models which include
explicit road source emissions (ADMS-Urban and ADMS-Urban RML) typically
capture the diurnal cycle of NOx, NO2 and O3
concentrations for the different site types, whilst for the regional model
this is only the case for background sites. The diurnal cycle for NO2
strongly reflects NOx emissions at all site types, showing morning
and afternoon traffic-related peaks, but also a dip around midday driven by a
peak in NO2 photolysis at this time. O3 peaks around midday
but concentrations are lower when NO2 traffic-related peaks occur.
Diurnal cycles are similar at both background and near-road sites, although
the NOx and NO2 peak-to-peak concentration ranges are lower
and O3 higher at background compared to near-road sites (as noted for
the annual average concentrations in Sect. 3.3). The observed diurnal cycle
of NOx concentrations at the BT4 site has a notably higher morning
peak than the cycle for the average over all near-road sites; this is less
pronounced for NO2 concentrations.
It is apparent that all of the models tend to overestimate O3 when
underestimating NO2, especially for near-road sites, as noted for
annual-average comparisons in Sect. 3.3. At BT4, the time-variation profile
of NOx is not well captured by the models, but appears closer for
NO2 and O3.
Discussion and conclusions
This study presents a regional-to-local air quality modelling system which
couples the regional EMEP4UK model with the fine-scale urban model
ADMS-Urban. Model simulations of NOx, NO2, O3, CO,
PM10 and PM2.5 using the coupled system are compared with the
regional and urban models run separately and with measurements from
background and near-road sites across London for 2012. This choice of base
modelling year has allowed detailed assessment of the model chemistry schemes
with the ClearfLo summer and winter intensive observation data (Bohnenstengel
et al., 2015; Malkin et al., 2016). During the summer of 2012 London hosted
the Olympic and Paralympic Games, but no effects from the games' periods were
apparent in a comparison of modelled and monitored concentrations at the
monitoring site closest to the Olympic Park (now Queen Elizabeth Olympic Park).
The simulations make use of an emissions inventory in which road traffic
emissions of NOx, NO2, PM10 and PM2.5 were
adjusted to represent real-world conditions. Using the stand-alone version of
ADMS-Urban these were shown to substantially improve both the fractional bias
and normalized mean square error but had little effect on correlations with
measured data as these depend on the relative changes in emissions hour by
hour which were unaffected by the adjustment.
From the results using the coupled model it is estimated that 13 % of the
area of London exceeds the EU annual average limit value of
40 µg m-3 for NO2 in 2012. This is consistent with
the UK report to the EU of the Greater London urban area exceeding both the
annual average and hourly average limit values (Defra, 2013a). In contrast,
concentrations of PM2.5 and PM10 in London are estimated to have
negligible exceedances of the annual average limit values of 25 and
40 µg m-3 respectively.
The performance of the different modelling approaches used in this study
varies depending on the relative importance of regional and local emissions,
chemistry and transport processes for different pollutants and site types.
The concentrations of the gaseous pollutants NOx, NO2 and
CO are dominated by local emissions. This is particularly clear for
NOx and NO2, with large absolute and relative increments in
concentration between background and near-road sites. The regional model
(EMEP4UK) performs well at background sites but underestimates concentrations
of these gases at near-road sites, due to the low resolution of its input
emissions data which does not represent individual road sources. The urban
(ADMS-Urban) and coupled models both show good agreement compared to
measurements at both site types due to the inclusion of explicit source
emissions. This means that the coupled model system can be used with
confidence for locations or time periods where rural upwind measurements are
not available for use in ADMS-Urban or for assessment of impacts of future
emissions or climate change.
The model performance statistics for NO2 are generally better than
those for NOx for all models. This is in part due to the reduced
sensitivity to NOx emissions of NO2 concentrations relative
to NOx concentrations, as exemplified by the analysis of the
emissions adjustments. However, the clear inverse relationship between model
biases for NO2 and O3 is consistent with the local chemistry
generally being well modelled, with uncertainty in NO2 and O3
being related to uncertainty in NOx. Comparison between the coupled
and stand-alone urban models and measurements of average diurnal profiles of
NOx concentrations suggest the models are capturing the measured
features of the profiles, although there is some underestimation in
NOx at roadside around midday.
The concentrations of PM10, PM2.5 and O3 show more
influence from long-range transport than the other gaseous pollutants. Hence,
the coupled model results for these species are strongly affected by the
regional background and the regional model simulation of PM10,
PM2.5 and O3. For the coupled model, simulated O3 agrees
well with measured O3 at the background sites, but simulated
PM10 and PM2.5 are largely underestimated compared to background
site measurements. However, the coupled model still shows a significant
improvement compared to the regional model for simulated particulate
concentrations, especially at near-road sites, because it includes an
explicit representation of local source emissions. The average increment in
PM2.5 concentrations between background sites and near-road sites is
much smaller than for NO2 but is well represented by the coupled
system.
The ability of the models to simulate high concentrations has also been
investigated. In general the high concentrations are well simulated by the
three models and for all pollutants examined in this study except for
PM10, where the highest concentrations are due to local short-term
emission effects, for example construction dust, which is not included in
emissions inventories. For PM2.5, the urban model gives a reasonable
value of the RHC metric and hence of high concentrations, which indicates
that these are due to long-range transport, such as from forest fires, which
is captured by the measured upwind rural background concentrations but not
necessarily by the regional model. The general tendency of the EMEP4UK
regional model to underestimate particulate concentrations, both long-term
and episodic, has been identified previously (Lin et al., 2017; Vieno,
2016b), while all models are affected by local emission effects and
uncertainties in measured particulate concentrations (Pernigotti et al.,
2013).
Representing the time variation of emissions accurately, including the
variation between sites and pollutants, is a challenge for all models and
particularly affects the correlation values. In the current work a single
time-varying profile was used for all road emissions in the urban and coupled
models but the modelling would be improved if more detailed profiles were
included. However, no time variation data are currently associated with the
LAEI. A further model performance evaluation that would be of great interest
would be to assess the models against measurements over a wider range of
heights, as model predictions are routinely used to calculate building facade
concentrations within street canyons. Some very high measurements were
carried out during the ClearfLo project, at a height of 180 m on the BT
tower, but the authors are not aware of any measurements covering the range
of average building heights in central London of 20–40 m (as shown in
Fig. 3).
Overall, this study has shown the benefit of coupling a regional atmospheric
chemistry transport and dispersion model with a local model in order to
calculate detailed spatio-temporal distributions of air pollutants. Such
detailed pollutant spatial distributions have applications in health-related
exposure analysis (Smith et al., 2016). The coupled system could also be used
to assess the effects of air quality policies at a range of scales. An
extension to the current study would be to process the hourly model output to
assess the exceedance of short-term objectives and combine the results with
population data to calculate exposure. The work presented in this paper
provides a framework for more detailed examinations of urban atmospheric
chemistry, in particular the effects of additional species which interact
with NOx, NO2 and O3, and for studies of the
effects of the urban heat island and future climate on urban air quality and
chemistry.