Introduction
Limit values for ambient NO2 concentrations (Ambient
Air Quality Directive 2008/50/EC) as well as NOx exhaust emission
standards are set by European legislation, but ambient measurements show that
NO2 concentrations still frequently exceed the European annual mean
limit value of 40 µgm-3 . For
example, 12 % of all measurement sites in Europe registered exceedances of
the annual mean limit value in 2014, most of them located at the roadside.
Within Europe, Germany had the highest median NO2 concentrations in
2014 , where it was estimated that the limit value was
exceeded at 57 % of all traffic sites . While there is a
downward trend in NO2 concentrations due to decreasing
NOx emissions, extrapolating the current trend to 2020,
exceedances are still expected at 7 % of the stations in 2020, requiring
additional measures in order for the European air quality goals to be met
.
In general, traffic is the most important source of NOx emissions
in Europe, contributing 46 % in 2014 in the EU-28, with considerably higher
contributions to ambient NO2 concentrations in urban areas
. NOx emissions from diesel vehicles, the main
traffic NOx source, have recently been a strong focus of
international media attention: despite increasingly strict emission standards
for diesel cars with the introduction of the Euro 5 and Euro 6 norms, under
real-world driving conditions, i.e. the pollutants a car produces while being
driven on real roads as opposed to being tested in a lab, Euro 5-certified
cars exceed the emission limit of 0.18 g km-1 by an average factor of
4–5 e.g. and the newer Euro 6 cars exceed
the emission limit of 0.08 g km-1 by an average factor of 6–7
e.g..
NOx impacts human health, ecosystems and climate directly and
indirectly as a precursor of tropospheric ozone (O3) and particulate
matter (PM). Health impacts of NO2 include adverse respiratory
effects , and the effect of road traffic NO2 on
premature mortality might be more than 10 times larger than the effect of
road traffic PM2.5 .
In order to support policy makers in identifying suitable measures to reduce
roadside and urban background NO2 concentrations to levels well below
the limit value, as well as to assess the health impact of current and future
NO2 concentrations, air pollution modelling is a valuable tool
e.g.. Chemistry transport models can be used to
assess the impact of local emissions on air chemistry and air quality in the
surroundings and downwind of the emission sources. Online-coupled models,
such as the chemistry version of the Weather Research and Forecasting model
(WRF-Chem, Grell et al., 2005), have several advantages compared with offline
approaches. These include, for example, a numerically more consistent
treatment and a more realistic representation of the atmosphere, particularly
in case of high model resolution .
Due to its short lifetime in the atmosphere, NO2 is more spatially
variable than for example O3, particularly in urban areas with
locally high NOx emissions. This is one of the reasons why models
with higher spatial resolutions of a few kilometres are capable of
representing observed NO2 concentrations better than coarser models,
with better performance if emission input and meteorological data are also
available at the high resolution e.g.. In terms of
model evaluation, comparing NO2 concentrations averaged over a coarse
model grid cell with point measurements can lead to mismatches
, with a better comparability achieved through high model
resolutions of only a few kilometres or less, depending on the size of the
city. Simulating air quality in Mexico City, showed that
reasonable model results can be achieved at a ratio of city size to model
resolution of ca. 6:1.
However, many modelling studies report discrepancies between modelled and
observed NO2 concentrations, which are in parts attributed to an
underestimation of traffic NOx emissions. All but one model
simulating the European domain during model intercomparison project AQMEII
phase 2 underestimate annual mean NO2 concentrations by 9–45 % on
average. Some of them overestimate NO2 concentrations at nighttime
, meaning that daytime concentrations are underestimated even
more than the average model bias would indicate. Similarly, the European
models contributing to the more recent AQMEII phase 3 intercomparison show an
underprediction of NO2 concentrations throughout the whole year, with
the sole exception of one model . In the Eurodelta model
intercomparison study , the participating models
simulate NO2 concentrations reasonably well on average compared with
observations in the rural background, but most models show an underestimation
of daytime NO2 on average, particularly in summer Fig. 9
from. Few studies focus particularly on NO2 in
urban areas: simulated air quality over Europe at a
horizontal resolution of 0.125∘ × 0.0625∘ with the
CHIMERE model for 2009 and found that NO2 concentrations are
underestimated by more than 50 % in urban areas. show
that the bias in modelled NO2 concentrations in urban areas is
reduced with increasing model resolutions, but still report negative biases
for a model resolution of 7 km × 7 km, between 6 and 10 µgm-3 for different offline-coupled chemistry transport models.
report a negative bias in NO2 concentrations
simulated with WRF-Chem at 3 km × 3 km of ca. 50 % on average
and up to 60 % during daytime. assess the impact of
different diesel NOx emission scenarios on air quality in
Antwerp, combining model simulations with LOTOS-EUROS at a horizontal
resolution of ca. 7 km × 7 km (urban background) with a street
canyon model. They report a low bias in modelled urban background NO2
concentrations of ca. 20 %, requiring bias correction for the further
analysis of the emission scenarios. evaluated air quality
simulated with WRF-Chem over the Berlin–Brandenburg region and found
underestimations of NO2 concentrations at daytime, and
overestimations at nighttime.
Many studies attribute an underestimation of observed NO2
concentrations to an underestimation of emissions e.g. and particularly traffic emissions in urban
areas . Further reported causes of the disagreement
include problems with simulating the correct PBL height and mixing in the
model e.g..
Modelling studies for North America report lower negative or even positive
biases in modelled NO2 concentrations e.g..
While total NOx emissions reported for Europe are on average
already larger than for North America by a factor of more than 2
, these differences might indicate an even larger contribution
of diesel car emissions to measured NO2 concentrations, as the share
of diesel cars is a main difference in emission sources between Europe and
North America. Thus, large differences in the model bias between Europe and
North America would be consistent with an underestimation of diesel traffic
emissions in Europe.
Emissions are typically estimated from a combination of activity data (e.g.
fuel burnt) and emission factors. Emission factors for road transport
emissions depend on the fuel type and the car type (heavy duty or light duty,
exhaust treatment) as well as on the driving conditions, including road type
and speed e.g.. While activity data are only
assumed to have an uncertainty of ca. 5–10 %, the emission factor is more
difficult to quantify in many cases and references
therein. Emission factors for road transport, for example, may
have an error range between 50 and 200 %, while emission factors for energy
industry emissions, the second largest source of NOx emissions in
Berlin, are much better constrained, with an error range between 20 and
60 % . Emission error ranges for the TNO-MACC III
inventory used in this study are determined following the EEA Emission
Inventory Guidebook, and depend, for example, on the number of measurements
made for deriving the emission factor . Recent
studies for London show that NOx emissions from flux measurements
are up to 80 % , or a factor of 1.5–2
higher than NOx emissions from the UK National Atmospheric
Emissions Inventory, with the largest discrepancies found in cases where
traffic is the dominant source of NOx concentrations.
conclude from eddy covariance measurements in Austria that
traffic-related NOx emissions in emission inventories frequently
used by air quality models can be underestimated by up to a factor of 4 for
countries where diesel cars represent a major fraction of the vehicle fleet
and have a significant contribution to reported biases in modelled
NO2 concentrations.
In this study the aim is to quantify the underestimation of traffic emissions
in a widely used state-of-the-art emission inventory based on officially
reported emissions, for simulating NO2 concentrations in an urban
area with high resolution. We use the Berlin–Brandenburg area as a case
study, and use the WRF-Chem model to simulate NO2 concentrations. The
model set-up, model simulations and input data are described in Sect. 2, and
observational data used are described in Sect. . The
emission inventory used here is the TNO-MACC III inventory
, downscaled to ca. 1 km × 1 km for the
Berlin–Brandenburg area , also described in
Sect. . The analysis builds on advanced model evaluation
techniques, including an operational and a diagnostic evaluation (outlined in
Sect. ) of the modelled NO2 concentrations
, with the aim of assessing the contribution of different
sources of model error (Sect. ). Based on this analysis,
a correction factor for traffic emissions is calculated, and additional
sources of the model bias are discussed (Sect. ). The
factor is then tested in two individual 1-month long simulations for January
and July 2014, in the following referred to as sensitivity simulations. In
addition, we analyse observational data of NO2 concentrations and
traffic counts, assessing the linear scaling assumed between emissions and
traffic counts for the temporal distribution of emissions in chemistry
transport models. Section closes with a summary and
conclusions from the results.
Observational data
AirBase observations and NO2 uncertainty
NO2, NOx and O3 measurements are taken from
AirBase , a database compiling air quality observations from
the EU Member States and associated countries, performed as required by EU
clean air legislation. The files can directly be downloaded from the AirBase
website. In the case of Germany the measurements are performed by the federal
states. For the comparison with model results, observations from stations
within Berlin and in the adjacent surroundings in the Federal State of
Brandenburg representing “urban background”, “suburban background” and
“rural near-city” conditions are used (Fig. ;
also see Supplement Fig. S4 and Table S1). For our analysis, we re-classify
AirBase station DEBE066 in Berlin–Karlshorst from “urban background” to
“suburban background”, as the station is not located in the core area of
the city and pollutant concentrations measured there are similar to
concentrations measured at other suburban background stations. As a result,
four stations for each classification type are used in this study: Amrumer
Straße (DEBE010), Brückenstraße (DEBE068), Belziger Straße
(DEBE018) and Nansenstraße (DEBE034) in the urban background,
Blankenfelde-Mahlow (DEBB086), Buch (DEBE051), Groß Glienicke (DEBB075)
and Johanna und Willy Brauer Platz (DEBE066) in the suburban background, and
Frohnau (DEBE062), Grunewald (DEBE032), Müggelseedamm (DEBE056) and
Schichauweg (DEBE027) in the rural near-city background.
Locations of measurement stations in and close to Berlin, including
their AirBase station area classification and type and the land use classes
in Berlin according to the .
In addition, five measurement stations representing “traffic” conditions
within Berlin, which are located next to major roads within the core area of
the city, and assumed to be primarily influenced by traffic emissions, are
used for the observation-based analysis (Sect. ).
NO2 concentrations used for this study were measured using
chemiluminescence. With this method, NO2 is converted to NO with a
molybdenum converter before being detected using chemiluminescence, as NO
reacts with O3 to form NO2 and O2 while emitting
light see e.g.. A limitation of this
method is that other nitrogen-containing species (PAN, HNO3) are
also converted to NO in this process. In a comparison study,
found that only 73–82 % of the NO2
measured with this method is “real” NO2, at a rural background site
in Switzerland. However, they state that reasonable results are obtained with
this type of converter at urban background sites.
compared NO2 concentrations in urban smog conditions in Santiago de
Chile using chemiluminescence detection with a molybdenum converter and
differential optical absorption spectroscopy and found large differences
between measured concentrations during daytime. Further sources of
uncertainty are introduced in the detection itself, for which NO reacts with
O3, producing the luminescence signal to be detected.
assess the uncertainty of NO2 measurements, and
derive a simplified procedure in order to calculate
the NO2 measurement uncertainty, which we apply in order to obtain a
rough estimate of the uncertainty range of NO2. Accordingly, the
uncertainty (u) of the observed NO2 concentrations x at time i
is quantified as follows:
u(xi)=urRV⋅(1-α)xi2+α⋅RV2.
Here, urRV is an estimate of the relative uncertainty
around a reference value RV, and α is the fraction of uncertainty not
proportional to the reference value. We use the coefficients corresponding to
the mean uncertainties of the individual parameters, i.e.
urRV=0.09, α=0.06 and the reference value
RV = 200 µgm-3 .
Meteorological data
In order to complement the analysis and to investigate potential influences
of the modelled meteorology on modelled NO2 concentrations, we
include a comparison of modelled meteorology with observations. This includes
observations of 2 m temperature, and 10 m wind speed and direction, all
provided by the German Weather Service and available online
. In addition, mixing layer height derived from ceilometer
measurements at Nansentraße during the BAERLIN2014 campaign
are used for a qualitative comparison with the modelled
mixing layer height seefor a discussion of this type of
comparison. The data are generally available between 20 June and
27 August 2014, but include a number of gaps.
Analysis and evaluation metrics
Analysis of model results
Modelled NO2 concentrations are evaluated with the aim of using the
model set-up for policy-relevant analyses of urban NO2 concentrations
and NO2 reduction measures with high temporal and spatial resolution,
and in order to identify the main sources of the errors in modelled
NO2 concentrations. For this, we use both operational and diagnostic
evaluation metrics, which are explained in the following.
Operational evaluation metrics applied here are based on
and . They include an analysis of the mean bias (MB)
and normalized mean bias (NMB), the correlation coefficient (R), and the
root mean square error (RMSE, as defined in the Supplement). The model error
is compared with the model quality objective (MQO) and performance criteria
calculated from NO2 observations and their uncertainty. The MQO is
defined as follows:
MQO=12RMSERMSU,
with RMSU being the root mean square of the measurement
uncertainty. Following and , a MQO lower than 0.5
indicates that the model results are on average within the range of the
measurement uncertainty, and further efforts to improve model performance are
not meaningful. A MQO between 0.5 and 1 indicates that the uncertainties of
model and observations overlap, and that the model might still be a better
predictor of the true value than the observations. A MQO greater than 1, on
the other hand, indicates significant differences between the model and the
observations.
The performance criteria for mean bias, normalized mean bias and
correlation coefficient as defined in are listed in
the Supplement. As the uncertainty of NO2 measurements is partly
concentration-dependent, the MQO and the other performance criteria differ
between station classes and seasons.
The operational evaluation and model quality objectives are intended to
support an assessment of the extent to which a model can be used for
policy-relevant analyses, but do not point to the underlying processes that
might lead to a disagreement between model results and observations.
Furthermore, the calculation of the NO2 measurement uncertainty
underlying the calculation of the MQO and performance criteria is also based
on a number of uncertain parameters.
We thus complement the analysis with a diagnostic evaluation, comparing the
individual spectral components of the modelled and observed time series. This
is done following and : we use a
Kolmogorov–Zurbenko filter , a widely used filter in the
analysis of air quality data based on calculating the iterative moving
average of a time series, in order to decompose the modelled and observed
time series into contributions from different timescales. The
Kolmogorov–Zurbenko filter is a low pass filter, with the length of the
moving average window and the number of iterations determining the spectral
component to be filtered. Taking the difference between two filtered time
series (band-pass filter) makes it possible to decompose the observed and
measured time series into an intra-diurnal component (ID, < 0.5 days), a
diurnal component (DU, 0.5–2.5 days), a synoptic component (SY,
2.5–21 days) and a long-term component (LT, > 21 days) with the property
TS(x)=LT(x)+SY(x)+DU(x)+ID(x).
Here, TS describes the full time series of species x. This is described in
detail in and , and references
therein. Further details are also given in the Supplement.
By assessing the error of each component individually it is then easier to
relate the error to the model process(es) characteristic at the respective
timescale. The error analysis of the different spectral components is done by
“error apportionment” , breaking down the mean square
error (MSE) into bias, variance (σ) error and minimum achievable mean
square error (mMSE) as follows:
MSE=(mod-obs)2+(σmod-rσobs)2+mMSE.
As described by , the minimum achievable mean square error
is determined by the observed variability that is not reproduced by the
model. While this approach helps in investigating the sources of model
errors, it does not allow for clear identification or quantification of them
as several processes take place on similar timescales, and because this
filtering method does not allow for a complete separation of the different
spectral components seefor a discussion of this issue.
In addition to this operational and diagnostic analysis of simulated
NO2 concentrations, we include a brief evaluation of selected key
meteorological parameters (temperature, wind speed and direction) as well as
further chemical species (O3, NOx), the former because
WRF-Chem is an online-coupled model, and the latter because NO2 is
tightly linked to NO and O3.
Observation-based analysis
As traffic emissions are the focus of this study, the analysis of the model
results is complemented with an analysis based on observations of roadside
and urban background NO2 concentrations and traffic counts. Like in
many chemistry transport modelling studies, we assume a linear scaling of
traffic emissions with traffic counts, which are used as a proxy for
calculating time profiles of traffic emissions for each month, day of the
week and hour of the day. While it has been shown that model results can be
improved by taking into account country-specific driving patterns as well as
by applying separate diurnal cycles for heavy and light duty vehicles
, local traffic conditions (e.g. congestion) are currently
not taken into account in the calculation of the diurnal cycles.
Using observations of traffic counts and roadside NOx
concentrations in Berlin obtained at the same locations and times (data
described in Sects. and ), we assess
how much of the observed variance in NOx concentrations can be
explained with traffic counts in a linear model. In addition to a linear fit,
other types of relationships (e.g. quadratic, exponential) are also explored.
We neglect other influences on observed NOx concentrations such
as other emission sources and large-scale and local meteorological
conditions. In order to account for different conditions at different hours
of the day, we fit the data separately for each hour of the day. The
intention of this analysis is not to build a statistical model for roadside
NOx concentrations, but rather to give insight into the type of
relationship between roadside NOx concentrations and traffic
counts, complementing the model simulations done in this study.
Model evaluation
Meteorology
An in-depth evaluation of modelled meteorology obtained with a similar model
set-up is presented in for the summer (JJA) of 2014. Here,
model results for the whole year of 2014 are presented and discussed. Changes
in the model set-up compared with the set-up presented in
are the planetary boundary layer scheme (MYNN,
instead of YSU, ) and re-initialization of the model
meteorology every 2 days as described in Sect. . Tests
showed that though the change in the planetary boundary layer scheme did not
introduce considerable improvements, it did seem to lead to a slightly better
match of model results with observations in the timing of the decrease in the
boundary layer in the evening. Here an additional brief model evaluation is
done in order to ensure that the modelled meteorology still reproduces
observations reasonably well.
Modelled meteorology compared to observations and annual and
seasonal performance indicators. Mean bias (MB) and root mean square error
(RMSE) are indicated in K (temperature) and m s-1 (wind speed); the
normalized mean bias (NMB) and correlation coefficient (R) are unitless.
Data are aggregated as follows: MAM – March, April, May, JJA – June, July,
August, SON – September, October, November, and DJF – December, January,
February.
Temperature
Wind speed
MB
NMB
RMSE
R
MB
NMB
RMSE
R
Lindenberg
2014
-0.66
-0.06
2.17
0.96
0.81
0.25
1.63
0.69
spring (MAM)
-0.48
-0.04
2.26
0.91
0.87
0.27
1.8
0.68
summer (JJA)
-0.89
-0.05
2.4
0.89
0.87
0.31
1.68
0.61
autumn (SON)
-0.55
-0.05
1.99
0.95
0.92
0.32
1.58
0.63
winter (DJF)
-0.7
-0.3
2
0.93
0.57
0.14
1.43
0.76
Potsdam
2014
-0.71
-0.06
2.25
0.96
-0.47
-0.12
1.4
0.69
spring (MAM)
-0.49
-0.04
2.16
0.93
-0.34
-0.08
1.42
0.73
summer (JJA)
-0.45
-0.02
2.1
0.91
-0.14
-0.04
1.41
0.55
autumn (SON)
-0.76
-0.07
2.34
0.94
-0.46
-0.12
1.27
0.62
winter (DJF)
-1.15
-0.44
2.42
0.9
-0.99
-0.2
1.47
0.79
Schoenefeld
2014
-0.61
-0.06
2.16
0.96
0.02
0
1.3
0.78
spring (MAM)
-0.12
-0.01
1.97
0.94
0.07
0.02
1.34
0.81
summer (JJA)
-0.63
-0.03
2.1
0.92
0.16
0.05
1.39
0.66
autumn (SON)
-0.73
-0.06
2.27
0.94
0.12
0.04
1.21
0.7
winter (DJF)
-0.98
-0.39
2.3
0.91
-0.31
-0.07
1.25
0.85
Tegel
2014
-1.25
-0.11
2.48
0.96
0.4
0.12
1.33
0.75
spring (MAM)
-0.83
-0.07
2.26
0.93
0.58
0.18
1.43
0.77
summer (JJA)
-1.02
-0.05
2.23
0.92
0.58
0.2
1.44
0.66
autumn (SON)
-1.44
-0.12
2.65
0.94
0.47
0.16
1.24
0.69
winter (DJF)
-1.72
-0.54
2.76
0.9
-0.08
-0.02
1.17
0.84
Tempelhof
2014
-1.21
-0.11
2.51
0.96
0.45
0.13
1.32
0.74
spring (MAM)
-0.67
-0.06
2.22
0.93
0.47
0.13
1.38
0.77
summer (JJA)
-1.17
-0.06
2.35
0.91
0.5
0.16
1.51
0.63
autumn (SON)
-1.38
-0.11
2.65
0.93
0.43
0.14
1.25
0.7
winter (DJF)
-1.63
-0.54
2.76
0.9
0.38
0.1
1.12
0.81
Modelled and observed 2 m temperature and 10 m wind speed are compared at
five stations run by the German Weather Service, including Schönefeld,
Tegel and Tempelhof in Berlin and Lindenberg and Potsdam outside of Berlin
(Table ). Across the stations, annual mean temperature is
simulated well, with mean biases smaller than -1 ∘C outside of
Berlin and just above -1 ∘C within Berlin. Modelled and observed
hourly temperatures correlate well with R=0.96 at all five stations. Small
seasonal differences exist, with somewhat higher biases in winter (as large
as -1.7 ∘C in Tegel) and somewhat lower biases in spring (e.g.
-0.1 ∘C in Schönefeld). Annual mean wind speed is somewhat
overestimated within Berlin (between 0.02 and 0.45 m s-1, or up to
13 %), with correlations of the hourly values between 0.74 and 0.78 within
Berlin. In winter, wind speed is slightly underestimated at two out of the
three stations within Berlin (-2 and -7 % at Tegel and Schönefeld,
respectively), while it is overestimated somewhat more in spring and summer
(up to 0.58 m s-1, or ca. 20 % in Tegel). In spring and summer, the
main wind directions are captured relatively well by the model (see Figs. S6
and S7 in the Supplement). In autumn, wind from the east, the main wind
direction, is modelled less frequently than observed, but wind from the
south-east is modelled too frequently compared with observations. In winter,
modelled wind comes from south and south-west too frequently compared with
observations, at the expense of south-easterly wind directions, as depicted
in Figs. S6 and S7. Compared with , an improvement in summer
mean bias in wind speed is seen; with the JJA mean bias between 0.3 and
0.4 m s-1 smaller than that of the comparable simulation in
at all Berlin stations, and JJA correlation coefficients
improved by ca. 0.1. This can probably be attributed to the continuous
re-initialization of modelled meteorology in this simulation.
In addition, modelled and ceilometer-derived mixing layer heights (MLHs) are
compared (Fig. S8 in the Supplement). Even though a quantitative comparison
between the modelled MLH and the MLH height derived from optical measurements
is difficult to interpret see, a qualitative comparison
of mean diurnal cycles gives insight into the timing of the deepening of the
MLH. The comparison shows that the modelled increase in the summer MLH in the
morning is too early, already starting at ca. 04:00 in the model. Though the
precise time of the observed MLH increase cannot be determined from the
available data, it takes place between 05:00 and 07:00 (Fig. S8 in the
Supplement). An early modelled deepening of the mixing layer might lead to
overly early and thus overly strong mixing of chemical species in the model.
Operational evaluation of simulated chemical species
Seasonally and station-class averaged performance metrics are listed in
Table for NO2, NOx and O3.
NO2 and total NOx are biased low throughout the seasons
and station classes, with the highest (absolute and relative) mean biases for
urban background stations both annually and seasonally. The model bias is
relatively low at rural and suburban background stations, with annual mean
biases of only up to -2.8 µgm-3 (-19 %). Correlation
coefficients of modelled with observed hourly concentrations are R=0.50 and
R=0.55 in the rural and suburban backgrounds, respectively.
Modelled chemistry, seasonal performance indicators (averaged for
each station class; each class includes four stations) and the model quality
objective for NO2. Mean bias (MB) and root mean square error (RMSE)
are indicated in µgm-3; the normalized mean bias (NMB) and
correlation coefficient (R) are unitless. Data are aggregated as follows:
MAM – March, April, May, JJA – June, July, August, SON – September,
October, November, and DJF – December, January, February.
NO2
NOx
O3
NO2
MB
NMB
RMSE
R
MB
NMB
RMSE
R
MB
NMB
RMSE
R
MQO
rural-
2014
-2.12
-0.16
10.2
0.5
-3.77
-0.23
15
0.48
5.02
0.11
22.49
0.7
0.78
near-city
autumn (SON)
-0.97
-0.06
9.97
0.48
-3.9
-0.19
16.2
0.45
11.96
0.41
22.71
0.66
0.76
backgr.
spring (MAM)
-2.91
-0.23
11.69
0.42
-4.26
-0.29
16.39
0.37
3.88
0.07
23.42
0.62
0.89
summer (JJA)
-2.38
-0.26
8.23
0.37
-2.88
-0.28
9.57
0.32
1.41
0.02
25.49
0.61
0.64
winter (DJF)
-2.2
-0.12
10.66
0.47
-4.08
-0.18
16.83
0.46
2.85
0.09
17.18
0.55
0.79
suburban
2014
-2.8
-0.19
10.67
0.55
-7.2
-0.35
20.13
0.48
4.88
0.11
22.45
0.7
0.8
backgr.
autumn (SON)
-0.76
-0.05
10.32
0.52
-7.92
-0.32
23.12
0.44
12.22
0.42
22.39
0.67
0.78
spring (MAM)
-4.41
-0.31
12.2
0.49
-8.25
-0.44
21.71
0.39
4.15
0.07
24.06
0.61
0.92
summer (JJA)
-2.88
-0.29
9.01
0.44
-5.12
-0.4
13.14
0.34
1.16
0.02
25.49
0.64
0.7
winter (DJF)
-3.14
-0.16
10.96
0.53
-7.57
-0.28
21.24
0.49
2.02
0.06
16.53
0.57
0.8
urban
2014
-7.83
-0.29
16.69
0.51
-15.84
-0.4
35.57
0.47
3.25
0.08
21.01
0.73
1.13
backgr.
autumn (SON)
-4.89
-0.17
13.9
0.55
-16.9
-0.36
37.3
0.48
9.09
0.37
19.69
0.71
0.95
spring (MAM)
-10.23
-0.38
19.71
0.51
-17.09
-0.47
40.68
0.4
3.07
0.06
22.62
0.62
1.32
summer (JJA)
-9.26
-0.41
18.16
0.36
-13.3
-0.47
28.92
0.24
-1.94
-0.03
24.85
0.6
1.28
winter (DJF)
-6.84
-0.22
14.05
0.53
-16.16
-0.34
34.41
0.5
2.93
0.12
15.35
0.58
0.93
NO2 at urban background sites is biased by -7.8 µgm-3 (-29 %) on average, with a higher negative bias in spring
(-10.2 µgm-3, -38 %) and summer (-9.3 µgm-3, -41 %) and smaller negative biases in autumn
(-4.9 µgm-3, -17 %) and winter (-6.8 µgm-3, -22 %). Modelled hourly concentrations correlate reasonably
well with observations in autumn, spring and winter (R between 0.51 and
0.55), but worse in summer (0.36).
Modelled hourly ozone concentrations correlate reasonably well with
observations at all station classes throughout the whole year (R between
0.70 and 0.73), but with lower correlations for individual seasons. This
shows that intra-seasonal differences are represented well by WRF-Chem, with
slightly worse representations of inter-seasonal variations. Modelled ozone
concentrations are biased high at most stations and in most seasons, with the
exception of a low bias in summer in the urban background.
For NO2, the MQO (Eq. ) is greater than 0.5, but smaller
than 1, both annually averaged and in all seasons at rural near-city
background and suburban background stations. For urban background sites the
MQO is larger than 1 both on annual average and in spring and summer, and
just below 1 in autumn and winter, emphasizing that the model performs
reasonably well in the rural and suburban background, but the disagreement
between model results and observations is larger in the urban background.
This suggests that processes or emissions typical for urban areas are an
important source of model error.
In order to test the sensitivity of the results to the selected chemical
mechanism, we compare modelled NO2 and total NOx
concentrations for July with two different chemical mechanisms: RADM2 (the
base configuration in this study) and MOZART. For all station classes in and
around Berlin, the modelled NOx and NO2 concentrations
only show very small mean differences of -0.04 to -0.4 µgm-3 (NOx) and -0.4 to
-0.5 µgm-3 (NO2, RADM2 –
MOZART). This suggests that the model bias in NO2 and total
NOx concentrations of the base configuration is not strongly
influenced by the choice of chemical mechanism, but rather results from other
sources of error.
Diagnostic evaluation of simulated NO2 concentrations
In order to further assess the model performance and identify the main
sources of the model bias, a diagnostic evaluation is done, by spectrally
decomposing the modelled and observed time series of NO2 and
analysing the type of error of each component.
Averaging the decomposed time series over each station class, the modelled
long-term (LT) and synoptic (SY) components as defined in
Sect. correlate well with the observations: the
correlation coefficient for the LT component is 0.83, 0.81 and 0.72 for rural
near-city, suburban and urban backgrounds, respectively, and 0.60, 0.63 and
0.65 for the SY component (Fig. ). This suggests that changes
on timescales of ca. 2.5 days to a few weeks are captured relatively well by
WRF-Chem, which includes for example the modelled synoptic (meteorological)
situation and is consistent with the good model performance in simulating
observed meteorology. The correlation coefficients for the diurnal (DU)
component are smaller, with 0.45, 0.52 and 0.48 for rural near-city, suburban
and urban backgrounds, respectively. This suggests that the model has more
difficulties in capturing variations on timescales of a few hours to 2.5 days
than on longer timescales. This might be related to the diurnal variations in
modelled mixing, but also to the diurnal cycle of emissions. Particularly the
latter is strongly influenced by traffic emissions in the urban area and
might also point to deviations of the model-prescribed diurnal cycle in
emissions from the real-world diurnal cycle.
Long-term and synoptic components of modelled (orange) and observed
(black) time series, averaged over all stations of each station class. The
shaded areas show the variability (25th and 75th percentiles) between the
different stations within each class. Note the variable y-axis.
With the procedure used for spectrally decomposing the NO2 time
series, the LT component is the only systematically biased component, with
the other components fluctuating around zero. Decomposing the model error
shows that the bias of the LT component has the largest contribution to the
error for urban background stations (ca. 30 %,
Fig. ). NO2 has a short lifetime and is
mainly influenced by local and regional sources. This means that the boundary
conditions are not likely to be a strong source of error. The negative bias
in the LT component is consistent with both problems in daytime vertical
mixing and an underestimation of emissions. As discussed in
Sect. , NO2 concentrations detected with
chemiluminescence using a molybdenum converter might be biased high due to
interferences with other nitrogen-containing species (e.g. PAN, HNO3)
and could further contribute to discrepancies between modelled and observed
NO2 concentrations.
Contribution of different types of error to the mean square error of
the model, per station class. The mean square error is divided into squared
bias (bias2), variance error (var2) and minimum mean square error
(mMSE) of the long-term (LT), synoptic (SY), diurnal (DU) and intra-diurnal
(ID) components (see Sect. for further details).
The second largest error at urban background stations and the largest error
at rural near-city and suburban background stations is the mMSE of the
diurnal component. This means that part of the observed variability is not
reproduced by the model and is consistent with the comparably lower
correlation coefficients of the diurnal component compared with the synoptic
and long-term components. relate this error to problems
in comparing single point measurements with model grid cell values
(incommensurability) and a disagreement in timing of modelled and observed
concentrations, amongst others. The incommensurability can, in the case of
NO2, come from NO2 observations being influenced by local
sources that cannot be captured by WRF-Chem run at a horizontal resolution of
3 km × 3 km. The temporal variation of modelled NO2
concentrations, in the case of the diurnal component, can be influenced by
the temporal profiles prescribed to the emission input data. Thus, the error
is consistent with problems in the prescribed diurnal cycles of emissions
including traffic emissions, but might also be related to a diurnally varying
bias in emissions.
At rural near-city background stations, there is a relatively large
contribution of the variance error of the diurnal component. This is probably
caused by an overestimation of the standard deviation of observed diurnal
components in autumn (Fig. S9 in the Supplement), particularly pronounced at
the site Frohnau in the north or Berlin, slightly west of the main emission
sources. This might be explained by the disagreement in modelled and observed
wind direction in autumn, leading to higher than observed NO2 peaks
in the model.
present a diagnostic model evaluation of the AQMEII phase
3 model simulations for the year 2010 and report the largest error of
modelled NO2 in winter, both for the European and North American
domains simulated in AQMEII. Our results show the opposite for urban
background stations (Fig. S9 in the Supplement): the model error, and
particularly the bias, is smallest in autumn and winter. While
attribute the winter bias to a potential underestimation
in residential combustion emissions, these seem to be captured comparably
well by the TNO-MACC III inventory in the case of Berlin. The re-distribution
of these emissions based on population density, as described in
Sect. , may also have contributed to a better spatial
representation in our study.
Diurnal and weekly variation of the model bias
The results from the operational and diagnostic evaluation of modelled
NO2 concentrations suggest that emissions within the urban area are a
main source of model error, both contributing to the model bias and the lower
correlation with observations. Traffic emissions have the largest
contribution to urban NOx emissions. As traffic emissions have a
distinct weekly and diurnal cycle, we additionally assess mean diurnal cycles
of modelled and observed NO2 concentrations as well as the
differences between weekdays and weekends. This also helps to further assess
the contribution of problems in modelled mixing to the model error. In
addition, we analyse the MQO and performance criteria separately for weekends
(Saturday and Sunday) and weekdays (Monday through Friday). Public holidays
that fall on a weekday are excluded from this analysis, as they were not
treated separately from regular weekdays in the emission processing.
Mean diurnal cycles of modelled (orange) and observed (black)
NO2 concentrations, by station class and weekday/weekend. Shaded
areas show the variability between the different stations' mean diurnal
cycles (25th and 75th percentiles). Grey lines show the mean modelled
planetary boundary layer heights at the respective grid points (scaled, but
the relative changes between different hours and seasons are maintained).
The comparison of mean modelled and observed NO2 diurnal cycles shows
distinct differences between station classes and weekend and weekday diurnal
cycles (Fig. ). The diurnal cycle of observed NO2
concentrations is modelled reasonably well for rural and suburban background
stations. In particular, nighttime concentrations are simulated well for
rural and suburban background stations, and mostly underestimated in the
urban background. Other WRF-Chem modelling studies often report too little
mixing at nighttime over urban areas leading to a strong overestimation of
observed concentrations. In this study as in other modelling studies using
WRF-Chem (Ravan Ahmadov, personal communication, 2017), a modification of the model code was applied in
order to increase nighttime mixing. This, in combination with a more
realistic vertical distribution of point source emissions (as described in
Sect. ), seems to improve model performance for
NO2 during nighttime. In addition, tests revealed that this change to
the model code does not impact modelled daytime concentrations.
During weekdays, there is an underestimation of the observed morning peak in
all seasons and at all station classes. Weekend diurnal cycles are modelled
well at rural and suburban background stations. At urban background stations
there is a larger disagreement between modelled and observed concentrations
throughout the whole day on both weekends and weekdays. The underestimation
of daytime urban background NO2 concentrations is particularly strong
in summer and spring. This might be explained by mixing over urban areas
during daytime that is too strong, caused for example by a turbulent
diffusion coefficient that is too large during daytime over urban areas in
the lowest model layer. Other modelling studies have reported similar
problems, reducing the coefficient over urban areas e.g. of the
CHIMERE model set-up used in. An onset of the deepening of the
boundary layer that is too early (Sect. ) might
further contribute to the disagreement in the modelled and observed morning
peaks. Overall, this discussion shows that the representation of vertical
mixing over urban areas might have to be improved to be physically more
consistent in regional models, for example by better taking into account
urban heat and momentum fluxes and treating the urban parameterization
consistently with chemistry. Measurements of vertical profiles of
NOx in cities, particularly in the planetary boundary layer,
would be helpful in order to evaluate the models and improve the
representation of surface NOx concentrations, as the NOx
profile in the lowest model layer is not resolved at the model resolution
used in this study.
The model underestimation of observed daytime NO2 concentrations at
urban background stations is stronger on weekdays than on weekends, and is
particularly noticeable during the morning hours. This is consistent with an
overall underestimation of emission sources active in the morning hours on
weekdays and potentially also a misrepresentation of the diurnal cycles of
emissions in the model: traffic emissions are distributed in the model
throughout the day using a linear scaling with traffic counts
(Sect. ), which might fall short of accounting for
relatively higher emissions during situations with high traffic and
associated congestion. This issue is further assessed in
Sect. .
Generally, throughout all seasons, the NO2 MQO is not met on weekdays
for urban background stations, but is smaller than 1 on weekends
(Fig. ). The patterns of the model–observation disagreement,
and particularly the weekend–weekday differences, are consistent with
traffic emissions as a main source of the bias, with a particularly large
contribution to observed urban background concentrations.
Skill of WRF-Chem in simulating daytime (06:00–17:00 UTC) observed
NO2 concentrations. The index represents the the model quality
objective for the root mean square error (Sect. ) and
the performance criteria for mean and normalized mean bias (described in the
Supplement), for weekend and weekday days and each
month/season.
Top–down quantification of NOx emissions from traffic
Calculation of a correction factor
The results from the operational and diagnostic evaluation of modelled
NO2 concentrations suggest that traffic emissions are a main source
of model error in the urban background: the bias and the mMSE of the diurnal
component have the largest contribution to the model error in the urban
background throughout all seasons, which is consistent with both an
underestimation of the magnitude of traffic emissions, and a problem with
their temporal distribution. This is further supported by the smaller
(absolute and relative) daytime bias of modelled NO2 concentrations
on weekends, where there is less traffic. In the following, we derive a
correction factor based on this model bias, which represents the degree to
which traffic emissions are underestimated in Berlin, but also takes into
account that other sources of model error are likely to also contribute to
this bias.
Besides biases in traffic emissions, problems in modelled mixing, which is
particularly relevant in summer and spring when the mixed layer is deeper
than in other seasons, might contribute to the model bias. Other
contributions to the NO2 bias might come from deviations of modelled
from observed wind speed in certain periods, and a potential overestimation
of NO2 in the observations by detection of other nitrogen-containing
compounds as discussed above. These sources of error are likely to impact the
model results equally on both weekends and weekdays, whereas an
underestimation of traffic emissions will have the largest impact on the
results on weekdays. For the quantification of the underestimation of traffic
emissions we assume that the weekend bias is entirely caused by
non-traffic-emission-related sources of error and thus use the difference
between weekday and weekend bias as an estimate for the traffic-related bias.
We use the weekday–weekend difference of the relative biases
(Fig. ), thus assuming that the model error due to other
sources than traffic emissions roughly scales with the magnitude of modelled
concentrations. These are both conservative assumptions, as the correction
factor would be much larger if the whole weekday bias was regarded as caused
by traffic emissions, and it would also be larger if the absolute
weekday–weekend difference was used.
Relative bias in modelled NO2 concentrations at urban
background sites in Berlin, averaged over each season, hour and
weekend/weekday. The boxplot shows median (line), 25th and 75th (box), and
5th and 95th (whiskers) percentiles of the hourly bias. Points show the mean.
The grey shaded area shows the time period considered for quantifying the
underestimation of daytime traffic emissions.
In order to estimate the correction factor for traffic NOx
emissions, we combine the weekday increment of the model bias as defined
above with the average fraction of NOx emissions from traffic to
total NOx emissions in Berlin. The nighttime model bias on
weekends and weekdays at urban background stations is of similar magnitude on
weekends and weekdays (Fig. ). A t-test shows that the
differences between weekday and weekend bias are not statistically
significant at a 95 % confidence interval after ca. 17:00 UTC and before
ca. 05:00 UTC (depending on the season). Furthermore, traffic emissions used
in the model contribute only little to the total NOx emissions
before 06:00 UTC. This suggests that an underestimation of traffic emissions
is only likely to have a significant contribution to the bias in modelled
NO2 concentrations between ca. 06:00 and 17:00 UTC. Within the core
area of the city where traffic is high (all areas within the “S-Bahn
ring”/main core of the city), the average contribution of traffic
NOx to total NOx between 06:00 and 17:00 UTC is
between ca. 30 and 55 %, depending on the month and hour of the day.
Seasonal average values over the indicated time period are used for the
calculation of the correction factor, with 37 % in winter, 47 % in summer
and 42 % in autumn and spring.
With the above assumptions, we quantify the underestimation of traffic
NOx emissions in the core urban area on weekdays between 06:00
and 17:00 UTC as follows, calculating a correction factor
fNOx:
fNOx=11+NMB⋅1st.
With the (negative)
NMB = mod-obsobs, and
st denoting the traffic share of NOx emissions.
Averaged over all urban background stations, and all seasons, as well as the
time period between 06:00 and 17:00 this results in a correction factor of
ca. 3. When averaged over all hours of the day, this factor corresponds to an
overall underestimation of NOx traffic emissions in the urban
centre by a factor of ca. 2, and an underestimation of all-source
NOx emissions in the urban centre by a factor of ca. 1.5.
In order to gain more insight into the underestimation of the NOx
emissions, we calculate a separate correction factor for each hour and season
based on hourly mean seasonal biases and traffic NOx emission
shares (Fig. S10 in the Supplement). The seasonal correction factors show a
small increase between 06:00 and 08:00 with a subsequent decrease, and then
remain relatively constant from 11:00 to 17:00. The diurnal variations of the
factors for the different seasons are qualitatively similar, and the factors
vary in magnitude within a range of ca. 1 between the seasons, with the
factors being larger in winter than in summer. The diurnal cycle of the
correction factor could be due to a diurnally varying importance of other
sources of the modelled NO2 bias than the traffic emissions, such as
mixing, but might also be due to a disagreement in the prescribed diurnal
cycle of traffic emissions with the real-world diurnal cycle of traffic
emissions. The seasonal differences can at least partly be explained with the
seasonally varying relevance of other sources of model error, such as mixing,
which has a bigger impact in summer and thus also leads to a bigger bias on
the weekends, reducing the weekday increment. The seasonal differences might
also be influenced by the temperature dependence of NOx emissions
in newer diesel cars , leading to higher
NOx emissions at colder temperatures, which are not captured by
the model.
Overall, the assumptions in these calculations are rather conservative:
assuming the weekend bias is not caused by an underestimation of traffic
emissions at all is likely to underestimate the effect of any traffic bias.
As mentioned above, using the absolute weekday increment of the bias would
also lead to higher correction factors. A further discussion of the model
bias and correction factor looking into potential reasons contributing to an
underestimation of traffic NOx emissions is presented in
Sect. .
Sensitivity simulation with increased emissions
Time series of hourly observed (black line) and modelled
NO2, comparing the base simulation (red) with the sensitivity
simulations (blue) using increased traffic emissions by a factor of 3 between
06:00 and 17:00 UTC on weekdays. The time series are averaged over all 4
urban background stations. Weekends are highlighted in dark purple, and
holidays are highlighted in light purple.
Statistics of modelled NO2 and NOx concentrations
for January and July, for the base simulation and for the sensitivity
simulation with increased traffic emissions, at the urban background stations
in Berlin. Mean bias (MB) and root mean square error (RMSE) are indicated in
µgm-3, the normalized mean bias (NMB) and correlation
coefficient (R) are unitless.
NO2
NOx
MB
NMB
RMSE
R
MB
NMB
RMSE
R
Amrumer Str.
Jan
2014_ref
-6.77
-0.21
11.93
0.65
-14.63
-0.31
28.85
0.65
2014_emis
-4.16
-0.13
11.09
0.68
-8.01
-0.17
26.18
0.64
July
2014_ref
-10.12
-0.47
17.88
0.34
-12.25
-0.49
22.88
0.27
2014_emis
-8.9
-0.42
18
0.33
-10.51
-0.42
23.21
0.25
Belziger Str.
Jan
2014_ref
-10.64
-0.32
15.39
0.51
-20.88
-0.41
34.23
0.51
2014_emis
-7.97
-0.24
14.04
0.53
-14.57
-0.29
31.6
0.51
July
2014_ref
-7.32
-0.37
15.32
0.31
-8.38
-0.37
17.81
0.23
2014_emis
-4.02
-0.2
16.73
0.22
-3.67
-0.16
20.43
0.12
Nansenstr.
Jan
2014_ref
-6.09
-0.21
11.12
0.61
-12.81
-0.3
25.05
0.56
2014_emis
-3.72
-0.13
10.28
0.64
-7.44
-0.18
23.28
0.55
July
2014_ref
-8.73
-0.43
15.33
0.42
-10.89
-0.45
19.18
0.35
2014_emis
-6.88
-0.34
15.43
0.36
-8.25
-0.34
19.37
0.28
Brückenstr.
Jan
2014_ref
-7.1
-0.23
12.51
0.57
-17.27
-0.36
36.4
0.51
2014_emis
-4.5
-0.15
11.13
0.63
-11.16
-0.24
32.58
0.54
July
2014_ref
-9.92
-0.46
17.24
0.25
-12.85
-0.49
22.7
0.17
2014_emis
-8.05
-0.37
16.87
0.26
-10.16
-0.39
22.13
0.18
The weekday correction factor was applied to NOx traffic
emissions for the core urban area of Berlin (within the “S-Bahn Ring”) and
tested in two sensitivity simulations for January and July 2014. The results
(Table and Fig. ) show that the bias
of modelled NO2 concentrations at urban background stations decreases
on average by 2.6 µgm-3 (NMB decreases from -24 to
-16 %) in January, and by 2.0 µgm-3 (from -43 to
-34 %) in July when applying the correction factors for NOx
emissions from traffic. The decrease is larger when only considering
weekdays, with a mean bias lower by 3.4 µgm-3 (from -26 to
-16 %) in January and by 2.7 µgm-3 (from -46 to
-34 %) in July. NO2 concentrations on weekends are still
represented reasonably well by the model in January
(Fig. ). The weekend bias is only changed (decreased) by
lower than 0.4 µgm-3 in both cases. Only a minor change would
be expected, since emissions on the weekend are not changed in the
sensitivity simulations, compared to the base simulation. In January, the
correlation of modelled with observed NO2 concentrations in the urban
background is improved by between 0.03 and 0.06 for urban background stations
in the sensitivity simulation, but this is not the case in July
(Table ). The lack of improvement in the July correlation
coefficient could be related to nighttime concentrations in July that seem to
be very sensitive to the increase in emissions during daytime
(Fig. , lower panel). Despite an improved representation
of nighttime concentrations compared to a previous study ,
this sensitivity suggests the need for further attention to mixing processes
in urban areas in high-resolution chemistry transport models.
Bigger improvements are seen when comparing total NOx: the mean
bias for urban background stations is reduced from -16.4 to
-10.3 µgm-3 (NMB decreased from -35 to -22 %) in
January and from -11.1 to -8.1 µgm-3 (from -45 to
-33 %) in July. Only considering weekday concentrations, these are
improved by 8.1 and 3.9 µgm-3 (from -37 to -12 and
-48 to -33 %) in January and July, respectively. The differences in
NO2 and NOx improvements suggest that the impact of the
primary NO2 fraction in emitted NOx on modelled
NO2 and NOx concentrations, as well as the influence of
chemical processes such as NO titration and other relevant physical and
chemical processes, might need to be assessed in greater detail.
While on average the normalized
mean bias in a modelled rural and suburban background is only reduced by
1–2 % in both January and July, the simulation of NO2 and
NOx concentrations downwind of the city centre is improved
considerably in the sensitivity simulation. To analyse the change in modelled
downwind concentrations, the results are broadly divided based on four main
wind directions (N, W, S, E). For each wind direction bin, the results of two
stations outside the core urban area are analysed, with Frohnau and Buch in
the north, Johanna und Willi Brauer Platz and Mueggelseedamm in the east,
Schichauweg and Blankenfelde-Mahlow in the south and Gross Glienicke and
Grunewald in the west. Only situations with wind speeds above 2 m s-1
are considered. The statistics are calculated for stations where at least 72
hourly model–observation pairs exist in the respective wind direction bin,
leaving four stations in January and six stations in July for the analysis,
with between 91 and 228 model–observation pairs. With some differences
between the stations, the bias of weekday downwind NO2 concentrations
was reduced by between ca. 1.5 and 2.9 µgm-3 January and ca.
0.4 and 1.5 µgm-3 in July. Thus, downwind NO2
concentrations in the sensitivity simulation are only biased by ca. -4 %
(January) and -14 % (July) on average (as compared to -12 and -22 %
in the base run). This shows that the increase in traffic emissions also
helps improve modelled downwind concentrations.
Overall, in both January and July, the bias in modelled urban background
NO2 and NOx is improved but still negative. Modelled
downwind NO2 concentrations are improved considerably, but with low
negative biases remaining also in this case. The improvements are consistent
with an underestimation of traffic emissions being a main source of error.
However, the results also suggest that on the one side traffic emissions
might still be too low, which is consistent with the correction factor being
a rather conservative estimate. On the other side, a still negative bias is
also consistent with other sources of error contributing considerably to the
model–observation differences as discussed previously. A relatively large
bias in July remains, consistent with the mixing being an additional main
source of error, particularly in summer.
Modelled O3 concentrations are not very sensitive to the changes in
NOx concentrations. On average, modelled O3 is reduced at
urban background stations in January by 1.5 µgm-3 (NMB
decreases from 29 to 22 %). In July, the increased NOx leads to
a reduction in the already negatively biased O3 from the model, with
the mean bias changing from -7.3 to -8.6 µgm-3 (-11 to
-13 %). Similarly, simulated O3 concentrations downwind of the
city (in analogue to the downwind NO2 concentrations described above)
are biased negatively in both the base run and sensitivity study in July. The
bias of downwind concentrations changes from -5.4 µgm-3
(-7 %) in the base run to -6.8 µgm-3 (-9 %) in the
sensitivity run. The negative bias in both NOx and O3 in
the base run is consistent with the model simulating insufficient
NOx emissions in a NOx-limited ozone production
regime. The reduction of O3 concentrations in response to increased
NOx emissions is however consistent with the model actually being
in a NOx-saturated (VOC-limited) ozone production regime. The
representation of VOC emissions in the model could play a role in explaining
this discrepancy, as for example biogenic VOC emissions in the
Berlin–Brandenburg urban area are underestimated when using WRF-Chem and
MEGAN . A comprehensive analysis of the simulated ozone
production regime is beyond the scope of this work.
Analysis based on traffic counts
The model bias and the calculated correction factors show a diurnal cycle,
with a larger model bias/correction factor in the morning hours. As explained
in Sect. and , one reason
for this might be differences between prescribed and real-world diurnal
cycles of the emissions. The diurnal cycle of traffic emissions in the model
is calculated based on traffic counts for Berlin, assuming a linear scaling
of traffic emissions with traffic counts, as done in many modelling studies.
Here, we use 3 years of hourly observations of roadside NOx
concentrations and traffic counts measured at the same stations in order to
get insights into the relationship between NOx concentrations and
traffic counts. A linear regression model does not explain the variance of
observed NOx concentrations at nighttime, as indicated by the
R2 close to 0 in Fig. . However, during daytime,
traffic counts alone explain up to ca. 40 % of observed NOx
variance, particularly during the traffic rush hours. The explained variance
is smaller during the afternoon peak. In comparison to a linear relationship,
a quadratic relationship (NOx ∝ (traffic_count)2)
does not explain more of the observed variance (not shown). An exponential
relationship (NOx ∝ exp(traffic_count)), however, does
explain a considerably larger share of the observed variance during daytime
and particularly during the traffic rush hours, as depicted in
Fig. (up to ca. 60 % depending on the station).
Comparison of R2 for linear and exponential fits of roadside
NOx concentrations with traffic counts.
This simple comparison suggests that roadside NOx concentrations,
and thus most likely also road transport NOx emissions, scale
more than linearly with traffic counts at times when the traffic intensity is
high and underline that the assumption of a linear scaling of traffic
emissions with traffic counts does not reflect the diurnal variation of
traffic emissions sufficiently. More highly congested roads are typical in
the morning, and emission factors (e.g. from HBEFA) are higher in congested
situations compared to free-flowing traffic. Differences in congestion could
contribute to explaining the non-linear scaling of NOx
concentrations with traffic intensity. While the impact might not be large
when simulating air quality with coarser models, it might play a more
important role in high-resolution air quality modelling, and the temporal
distribution of emissions could potentially be improved when taking these
differences into account.
Discussion of traffic emissions
Based on a comparison of modelled and observed NO2 concentrations, we
estimate that traffic emissions in the urban core of Berlin are
underestimated by a factor of ca. 3 on weekdays between ca. 06:00 and
17:00 UTC. This corresponds to an overall underestimation of NOx
traffic emissions (all-day average) in the urban centre by a factor of ca. 2,
and an underestimation of total NOx emissions (all-day average)
in the urban centre by a factor of ca. 1.5. Reasons for the underestimation
of emissions used in this study can include limitations in the applicability
of the emission inventory used here for high-resolution urban air quality
modelling, problems in the temporal distribution of emissions, but also a
general underestimation of traffic NOx emissions in the
inventories. These three points are discussed further in the following.
First, while a reasonably good model performance can be achieved using the
downscaled version of the TNO-MACC III inventory outside of the urban areas,
the deviations of modelled from observed NO2 in the urban background
might point to limitations in the applicability of these types of emission
inventories for high-resolution modelling of NO2 in urban areas. The
horizontal resolution of the original TNO-MACC III emission data is ca.
7 km × 7 km and national totals are disaggregated on the grid
based on traffic intensities. Spatial differences in congestion, with
emissions greatly varying between the different driving conditions and with
car speed e.g., are probably not well resolved. A
comparison of the downscaled version of the TNO-MACC III inventory for Berlin
with a local inventory has, however, not revealed major differences in road
transport emissions (see Sect. ), suggesting that a
static highly resolved local inventory based on detailed local information is
not likely to improve the model results by much.
Second, in addition to spatially unresolved differences in driving conditions
and related emission factors locally increasing the underestimation of
emissions, the diurnal cycle of the bias in all seasons suggest that the
diurnal cycle of traffic emissions also does not sufficiently account for
temporal differences in driving conditions. This is consistent with the
observation-based analysis, suggesting that observed NOx
concentrations do not scale linearly with traffic counts. While these
assumptions might be valid for coarser model resolutions, they may need to be
revisited when going to higher resolutions with a focus on urban areas.
However, modelled NO2 concentrations are broadly underestimated
throughout the day, which means that deviations of the model diurnal cycle
from the real-world diurnal cycle alone cannot explain the underestimation of
modelled NO2 and NOx concentrations.
Third, traffic NOx emissions may be underestimated generally by
emission inventories. The correction factor calculated here is in line with
the results from other studies quantifying traffic emission underestimations
in Europe, reporting traffic NOx underestimations of around
80 % , a factor of 1.5–2 and up to a factor
of 4 . A potential reason for the underestimation in
NOx emissions from traffic can be discrepancies between
real-world emission factors and those used in emission inventories. Even
though HBEFA emission factors, which are often used for calculating
emissions, are based on real-world driving conditions, the latest update of
the handbook reports higher emission factors than previously assumed for Euro
6 and Euro 4 diesel cars , e.g. an increase by ca.
50 % in case of Euro 6 vehicles Fig. 14 in. In
addition, the update assesses the temperature dependence of emission factors
and concludes that it may lead to increases in NOx emissions of
more than 30 %, compared with standard test conditions. NOx
emissions from diesel cars increase with decreasing temperatures
. This may also contribute to the larger correction
factor calculated for the winter months. Finally, while some amount of
congestion is included/assumed in the emission inventories, this might be an
aspect that is underestimated in terms of severity and extent.
The first and second points of this discussion suggest that improvements
might be achieved by combining high-resolution chemical transport models with
more detailed approaches of calculating emissions. Coupling with a traffic
model, for example, might allow for not only being able to take local
differences in traffic conditions into account, but also prescribe a more
realistic diurnal cycle of traffic emissions. Dispersion modelling and street
canyon modelling e.g. OSPM, often already take a
more detailed calculation of traffic emissions into account, and different
emission modelling approaches exist e.g. traffic models such as
MATSim,. The benefit of high-resolution chemistry transport
modelling, e.g. their ability to assess the impact of different emission
sources on air quality on larger scales and downwind of the main emission
sources, could be further exploited if coupled with existing, more detailed
approaches in calculating traffic emissions or general improvements in the
accuracy and resolution of emission inventories.
The consistent findings that inventories of European traffic emissions may be
underestimated, coming from studies using very different methodologies,
suggest that further research is necessary in order to understand real-world
traffic emissions and to represent them in the inventories accordingly.
Alternative measurement approaches could help verify the assumptions
underlying the calculation of emissions, and help identify potential
systematic problems.
Summary and conclusions
Several modelling studies, particularly for Europe, have reported an
underestimation of modelled NO2 concentrations compared with
observations. Measurement studies also suggest that there might be
considerable differences between measured urban NOx emissions and
emissions provided by emission inventories based on official reporting,
particularly when the contribution of traffic is large. This study quantifies
the underestimation of traffic NOx emissions using WRF-Chem in a
top–down approach, with the Berlin–Brandenburg area in Germany as a case
study. The emission inventory used here is TNO-MACC III, downscaled to
1 km × 1 km over the Berlin–Brandenburg area based on local proxy
data. The downscaled traffic emissions averaged over Berlin only differ by
6 % from a local bottom–up traffic emission inventory.
A diagnostic evaluation of the model results shows that particularly in the
rural and suburban background, the long-term and synoptic components
representing processes at timescales of the order of 2.5 to 21 (synoptic) and
longer than 21 days (long-term) are simulated well by the model. This
suggests that the modelled impact of meteorology on concentrations is
represented well overall. The largest contribution to the model error comes
from the (negative) bias in the urban background, and from deviations of
modelled from observed variability of the diurnal component (0.2–2.5 days).
This suggests a possible underestimation of urban emissions, of which traffic
is the single most important contributor to NOx emissions, but is
also consistent with deficiencies in other processes varying on the diurnal
scale such as the modelled mixing in the planetary boundary layer. The
analysis of the model results suggests that the latter is particularly
relevant in summer and spring, and that further research is needed in order
to better represent urban processes and their coupling with chemistry in WRF-Chem. For example, the
changes in the model code applied here to improve nighttime mixing can be
critically discussed, and would ideally be replaced by an improved
parameterization of urban processes. The latter would need to better account
for urban heat and momentum fluxes for a more realistic representation of
mixing both at daytime and at nighttime, particularly in summer. An
alternative model configuration to be tested could be the recently extended
ACM2 planetary boundary parameterization , which now
conducts mixing of chemical species within the planetary boundary layer
scheme. In addition, measurements of vertical profiles of NOx in
urban areas are needed to evaluate and improve models for applications in
urban areas.
The analysis of the diurnal cycle of the model bias as well as a simple
observation-based calculation showing that roadside NOx
concentrations scale non-linearly with traffic counts suggest that a further
source of error is likely the prescribed diurnal cycle used for traffic
emissions. In this study as well as in many other modelling studies, the
diurnal cycle of traffic emissions is calculated assuming a linear scaling of
traffic emissions with traffic counts. While this might be sufficient for
coarser model resolutions, high-resolution urban air quality modelling with
chemistry transport models might benefit from a more detailed temporal
distribution, not only taking into account traffic intensity via a scaling
with traffic counts, but also diurnal differences in congestion.
We quantify the underestimation of traffic emissions based on the finding
that the weekday bias in modelled NO2 is larger than on weekends and
that the contribution of traffic NOx to total NOx
emissions in the urban area is typically higher on weekdays. The results
suggest that traffic emissions are underestimated by ca. a factor of 3 in the
core urban area on weekdays when traffic is highest (06:00 to 17:00 UTC).
The underlying assumption is that other sources of model errors influence the
model bias equally on weekdays and weekends, with the underestimation of
traffic emissions having the largest effect on modelled NO2
concentrations on weekdays. This underestimation corresponds to an
underestimation of weekly mean traffic NOx emissions in the core
urban area of ca. a factor of ca. 2 and an underestimation of total
NOx emissions in the city centre by a factor of ca. 1.5. Two
sensitivity simulations for January and July 2014 with NOx
emissions from traffic scaled with the estimated correction factor show that
increased traffic emissions improve the model bias in NO2 and
NOx concentrations in both seasons in the urban background, and
also improve modelled downwind concentrations. The still negative bias is
consistent with the factor being a rather conservative estimate.
The emission inventory used in this study is based on officially reported
emissions by the individual countries, and the emissions are spatially
distributed by TNO based on proxy data. Assuming the quality and accuracy of
the proxy data are similar at least for larger German cities, and considering
that modelling studies for other German cities have also shown an
underestimation of simulated NO2 concentrations using the same
emission inventory, we would assume that the results found in this study for
Berlin may generally be transferrable to at least other German metropolitan
areas. The underestimation of NO2 concentrations throughout the day,
the consistency of the calculated correction factor with findings from other
studies and the improvement of model results applying the correction factor
suggest that more research is needed in order to more accurately understand
the spatial and temporal variability in real-world NOx emissions
from traffic, and apply this understanding to the inventories used in
high-resolution chemical transport models. Given the above considerations,
this not only holds for the urban area of Berlin, but for German and most
likely European metropolitan areas more generally.