High uncertainties affect black carbon (BC) emissions, and, despite its
important impact on air pollution and climate, very few BC emissions
evaluations are found in the literature. This paper presents a novel
approach, based on airborne measurements across the Paris, France, plume,
developed in order to evaluate BC and NO
Knowledge on pollutant emissions is a key element in the field of air pollution. It provides essential information on the contribution of various source sectors to pollutant levels, which is required for targeting emission reduction measures. Emission inventories are necessary input to chemistry-transport models (CTMs) which are important tools for atmospheric research and air quality management.
Among the various emitted species, black carbon (BC) aerosol is a chemical
compound of major importance. In air quality, it highly contributes to the
health risk (Peng et al., 2009) related to fine particulate matter
(PM
However, high uncertainties still affect BC emission inventories, making the
true forcing uncertain. As a product of incomplete combustion processes, BC
emissions at the global scale mainly originate from energy-related
combustion (e.g., on- and off-road vehicles in transport areas, biofuel and
coal in residential areas) and open burning (savannas and forest fires) (Bond
et al., 2004, 2013; Junker and Liousse, 2008; Lamarque et al., 2010). Global
BC emissions have most recently been estimated to be 7.5 Tg yr
Compared to BC, many more efforts have been made to assess NO
The evaluation of these inventories still remains a critical point since
emissions are generally not directly measurable. The use of CTMs for direct
comparisons between measured and simulated concentrations is mostly
inadequate for drawing precise conclusions on emission inventories because
concentrations measured at a receptor point cannot be unambiguously linked
to emissions at a point aloft because of mixing processes and chemical
transformations. In addition, CTMs and the meteorological input data they
are using have their own uncertainties. Different alternative approaches
have thus been developed. Concerning the BC emissions, useful information
may be gained from their evaluation relative to emissions of another compound
for which uncertainty in emissions is expected to be smaller. For example,
Zhou et al. (2009) derived BC emissions in two Chinese megacities from CO
emissions and measured BC
The aim of this paper is to evaluate emission inventories at the scale of a
large city. In this context, it presents an original methodology based on
airborne measurements in the city plume and chemistry-transport simulations.
It is applied to BC and NO
The method developed in this study aims at evaluating, at the scale of a large city, emission inventories of species that can be traced at the scale of a few hours, i.e., either a chemically inert (at the timescale considered) single compound or a conservative family of products all originating from a unique primary compound. The method is based on airborne measurements of such species in the megacity plume during the afternoon in a well-mixed convective boundary layer (BL); thus the vertical mixing can be considered as rather well established and consequently the measured concentrations at a particular altitude as representative of concentrations in the whole BL.
A CTM simulation, using the inventory to be evaluated, is used to simulate tracer concentrations in the plume. For both observations and simulations, along the flight path perpendicular to the plume, tracer concentrations above regional background and within the pollution plume can be integrated. The ratio of the simulated area over the measured area corresponds to a spatially averaged emission error factor (EEF) for the agglomeration for each flight. To achieve such a calculation, the plume needs to be well distinguishable from the background, which requires large enough local emissions in the city and a rather homogeneous background.
This method aims at reducing the influence of some errors in the CTM. By considering integrated peak areas over lateral transects across the plume, it allows for the effect of some potential errors in the structure of the simulated plume to be minimized (e.g., any error in lateral dispersion, reasonable errors in wind direction) and consequently focus more on emissions. However, several potential error sources still remain, and therefore need to be carefully investigated: (i) the wind speed, which directly determines the temporal window of emissions sampled during the flight; (ii) the degree of vertical mixing, which determines the representativeness of the airborne measured concentrations; (iii) the wet and dry deposition of the tracer, which can lead to discrepancies in the emissions factors if not well simulated by the model; and (iv) the boundary layer height, which directly affects the level of concentrations. These points will all be discussed in the next sections.
The methodology is applied in this paper to BC and NO
In the framework of the EU FP7 MEGAPOLI project (Baklanov et al., 2010), two 1-month intensive campaigns (July 2009 and January/February 2010) were organized in the greater Paris area to better characterize organic aerosol in a large megacity. The study presented here is based on observations obtained during the summer campaign.
Petzold et al. (2013) recently made some recommendations about the use
of the term “BC” for black carbon, distinguishing various terminologies
depending on the property used in the measurement technique: (i) the light
absorbing coefficient
Ground measurements of light absorption coefficient, EC and NO
Among the chemical data available in the Paris plume, NO
Various physical parameters are also measured in the ATR-42 aircraft at a 1 s time resolution including wind speed, wind direction and position of the
aircraft (longitude, latitude, height). BL height (BLH) estimations are
available at the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) (48.712
Three European anthropogenic emission inventories are evaluated in this
paper, all referring to year 2005:
The EMEP inventory (Vestreng et al., 2007), with a longitude–latitude
resolution of 0.5 An inventory developed partly in the framework of MEGAPOLI project by
TNO. The inventory for the year 2005 was constructed using official emissions
submitted by European countries (downloaded from EEA in 2009) in combination
with a gap-filling procedure using IIASA RAINS or TNO default data. The compiled
emission data were spatially distributed at a resolution of
1/8 A third inventory based on the TNO inventory and with the same 1/8
The same EC
The resolution of both the TNO and TNO-MP inventory is considerably improved
compared to the EMEP inventory. Despite its coarse spatial resolution, the
comparison of this latter inventory with the two other refined ones remains
relevant for several reasons: (i) before being applied to simulations,
emissions are downscaled to the air quality model resolution, here to a 3 km
horizontal resolution, using the 1
The spatial distribution of BC and NO
It is worthwhile noting that, as previously mentioned, both the PSAP and the MAAP instrument are based on the measurement of the light absorption, and observations should thus be referred to as EBC. However, as emission factors and source profiles used to build emission inventories are mostly expressed as EC (Vignati et al., 2010; Chow et al., 2011; H. A. C. Denier van der Gon, personal communication, 2011), the simulated “BC” should be regarded as EC. An ambiguity therefore arises from comparisons between observed EBC and modeled EC since they correspond to different quantities. This point is discussed in Sect. S1 in the Supplement and in Sect. 4.3.4. In the following, the term BC will be kept for convenience for both observations and simulations.
BC (left panels) and NO
In this paper, all simulations are performed with the CHIMERE CTM (Schmidt
et al., 2001; Bessagnet et al., 2009; Menut et al., 2013) (
The CHIMERE model allows for simulation of transport, gas-phase chemistry, some aqueous-phase reactions, and size-dependent aerosol species, including secondary organic aerosol, dry and wet deposition. It treats coagulation, absorption and nucleation aerosol processes. Inorganic aerosol thermodynamic equilibrium is calculated using the ISORROPIA model (Nenes et al., 1998).
In this paper, simulations are performed during the summer MEGAPOLI campaign
(July 2009) with a 5-day spin-up period. Two nested domains of increasing
resolution – CONT3 (0.5
Boundary and initial conditions are taken from LMDz-INCA2 global model for
gaseous species and LMDz-AERO for particulate species (Hauglustaine et al.,
2004; Folberth et al., 2006). The model uses the previously described
anthropogenic emission inventories, while biogenic emissions are computed
with MEGAN data and parameterizations from Guenther et al. (2006). In order
to investigate the influence of meteorology on results, two meteorological
data set are considered. The first has been produced with PSU/NCAR mesoscale
meteorological model (MM5; Dudhia, 1993), performed over three nested
domains with increasing resolutions of 45, 15 and 5 km, respectively, and
using Global Forecast System (GFS) data from the National Center for
Environmental Prediction (NCEP) as boundary conditions and large-scale data.
The second one has been produced with the Weather Research and Forecasting
model (WRF; Skamarock et al., 2005;
In this section, we first evaluate meteorological input data (Sect. 4.1). A
first simple approach is then applied to evaluate BC emissions against
NO
Statistical metrics are defined as follows:
Mean bias: Normalized mean bias: Root-mean-square error: Normalized root-mean-square error: Correlation coefficient:
In the above,
In this section, meteorological input data used in CHIMERE simulations, with both MM5 and WRF models, are evaluated against observations at the surface and in altitude.
Figure 2 shows comparisons between observations and simulations for meteorological parameters obtained at the SIRTA ground site. Statistical results are reported in Table 1, considering all hours as well as only the 06:00–14:00 UTC time period (referred to hereafter as morning hours); these are more relevant in our methodology since transport from the urban emission sources to the aircraft location occurs in the morning and the early afternoon.
Except for the first days of a continental northeasterly wind regime, the
period is dominated by an oceanic regime with west and southwest winds. The
MM5 model shows a negative bias of
Wind lidar observations are compared with
simulated wind speed in the first model layers (the first vertical layers in
CHIMERE are at 43, 118 and 248 m a.g.l.) (see Fig. S6 in the Supplement).
Due to a low vertical resolution in simulations, comparisons remain
qualitative. The MM5 and WRF models show quite similar patterns, but the MM5
model tends to give a higher wind speed at all levels, including at ground level.
Statistical results over the 06:00–14:00 UTC time window in the 110–210 m
altitude range and for the flight days are reported in Table S3 in the
Supplement. On average, low negative biases and reasonable NRMSE are
obtained with both the MM5 and WRF model. At the daily scale, biases on wind
speed remain below
Wind (at 10 m), temperature (at 2 m) and BLH time series in July 2009 at SIRTA.
Statistical results of MM5 (and WRF in parentheses) considering all
July hours and only the 06:00–14:00 UTC time window (
Wind speed simulation results are much better along the aircraft path (not
shown), all biases remaining below
BC emissions can be first evaluated at ground level relative to those of
NO
BC and NO
We now evaluate, in further detail, BC emissions relative to NO
Given all these elements, we thus consider the BC
Observed and simulated BC vs. NO
BC vs. NO
Observed (along the aircraft trajectory) and modeled (in the background, with the TNO-MM5 case) BC concentration for 10 (left panel) and 13 July (right panel). Paris and some other large cities are indicated. Simulated concentrations shown here are taken at 13:00 UTC on the fourth layer, which roughly corresponds to 470–870 m height. The solid black line corresponds to the flight path outside that layer (altitude above 870 or below 470 m).
Given these first results obtained at ground level, the alternative approach based on airborne measurements in the plume is now presented. The procedure is first described in detail, and the results are then shown and their uncertainties discussed.
As an illustration, the TNO/MM5 case for two flights on 10 and 13 July
is considered. Aircraft trajectories and BC concentrations during these
days are presented in Fig. 5. As previously mentioned, the inlet used to
collect BC particles is characterized by a 50 % passing efficiency
aerodynamic diameter of 5.0
Observed (in black) and simulated BC concentrations along the aircraft trajectory for 10 (left panel) and 13 July (right panel).
Concentration variations at the end of the flight correspond to a vertical profile up to 3 km a.g.l. performed by the aircraft. In this study we focus on the time period during which the aircraft altitude is rather constant (about 600 m a.g.l.). As briefly described in Sect. 2, the methodology consists in computing for each transect the plume integral of concentrations above background, this latter being estimated in both model and observations as the 30th percentile of concentrations in one transect (see Fig. S8 in the Supplement). Only points above the background value are taken into account and, additionally, some adjustments are made when winds bring plumes from other cities close to the Paris one.
Given that the aircraft does not exactly cross the plume perpendicularly,
but with an angle
The EEF is finally defined as
BC (top panel) and NO
BC and NO Mean: Confidence interval on the mean:
Mean BC emissions results show considerable contrast between inventories and
suggest on average a slight overestimation of the EMEP inventory (
Ratios of BC EEFs over NO
Such a high day-to-day variability both in individual compounds EEF and in their ratio was not expected, which raises the question of its origin: does it come from the real-world emissions (missing in the model emission input data), or is it induced by uncertainties in the methodology, or both? In the next subsection, the variability potentially associated with observations themselves is discussed, while the variability that may come from the methodology (e.g., model errors) will be investigated in Sect. 4.3.4.
When investigating ratios of the BC area over NO
On 1 July, BC measurements around Paris (and notably upwind of the city) show
rather high but noisy concentrations (see Fig. S10 in the Supplement), which
suggests a possible heterogeneity in the BC regional background. In our
methodology, a unique regional background value is estimated, based on the
whole flight. In the case of a rather slender BC plume coming from the north
in the direction of Paris and adding itself to the city plume, our procedure
would thus not be able to discriminate both. This may explain the high
BC
BC
Another possible source of variability in the BC
The methodology used to evaluate NO
The methodology does not evaluate annual monthly emissions alone but rather also a part of the applied diurnal emission profiles (see Sect. 4.3.3) and errors on wind speed may shift the time window over which emissions are sampled (in simulations with respect to reality). This causes an additional uncertainty to be all the more important as the time window is narrow and temporal emission gradients are strong. In addition, wind speed errors within the city directly determine the residence time of air masses close to emission sources and thus the degree of pollutant accumulation.
Significant wind speed NRMSE at the SIRTA site, both at ground level and at
altitude levels below 200 m a.g.l. (around 40–60 % and 10–60 %,
respectively), have been found (Sect. 4.1). These errors influence the
accumulation of emitted pollutants within the city, for which uncertainties
are thus probably quite important, as the accumulation time is at first
order inversely proportional to the wind speed. Thus, this uncertainty in
the local wind speed appears as an important source of uncertainty and
variability in the day-to-day EEFs. However,
biases in the wind speed are reasonable, for example mostly below
Another uncertainty source is related to wind speed errors at higher
altitudes (between the agglomeration and the measurement location) and
subsequent errors on the plume advection. Given the diurnal profile of
emissions and the variable emission time window sampled by the plane
depending on the wind speed (Sect. 4.3.3), an error in advection would shift
this time window toward earlier (later) emissions in the case of
negative (positive) biases on wind speed in altitude. This
error source thus appears all the more important that the gradient in the
diurnal emission profile is high in the sampled time window. Daily biases on
wind speed below
As previously highlighted, aircraft measurements are
expected to have a higher spatial representativeness than at ground level, but
this relies on the assumption that the vertical mixing in the BL is
correctly established, so that observations obtained in the plane, at an
altitude of about 600 m a.g.l., can be considered representative of those
in the whole plume. The vertical heterogeneity is expected to be the highest
above the city and to decrease gradually along the plume due to the
turbulent mixing and the absence (or the relatively poor contribution) of
fresh emissions at ground level outside the city. The vertical turbulent mixing
parameterization in the CHIMERE model follows the
The BLH is the other important parameter that requires correct modeling,
since it determines the volume into which the emissions will be diluted
within the plume. During early afternoon, lidar observations at the SIRTA and
LHVP sites showed an underestimation by the MM5 model, while significant
improvements are obtained with the WRF model but still with a negative bias
at the SIRTA suburban site (Sect. 4.1). If such an underestimation exists in
the whole flight region, it may lead to an overestimation of emission biases.
However, processes are not linear, since the increased concentrations due to
a lower BLH may, for instance, be reduced by a higher dry deposition (which
depends on concentrations in lowest level). In order to assess the importance
of these errors, a sensitivity test is performed with the EMEP/MM5 case by
increasing the BLH by 30 % (corresponding to the mean bias between 06:00
and 14:00 UTC). So far, simulated cases have been performed with prognostic
turbulent parameters (i.e., directly taken from meteorological models).
However, as the diffusivity coefficient depends on the BLH, the sensitivity
test with BLH multiplied by 130 % is performed with the diagnostic
option, in which
BC and NO
As previously mentioned, an additional uncertainty may arise from the
comparison between EBC (observations) and EC (emissions and simulations)
through the MAC value used to convert absorption coefficients into EBC
concentrations. Airborne PSAP EBC concentrations have been obtained
considering a constant MAC of 8.8 m
Results obtained for each compound in Sect. 4.3.2 consist of mean error factors and rather large confidence intervals that result from (i) uncertainties associated with the day-to-day variability which is not included in the model input data (beyond the temporal dependence on the month and the day of the week), (ii) measurement uncertainties and (iii) uncertainties in the methodology (conditioned by error sources in the model).
The first type of uncertainties is difficult to quantify but can be reasonably considered random. Also, measurement uncertainties are probably mostly random, but they may include a part of systematic uncertainties. In order to be conservative, they are assumed to be entirely systematic. Uncertainties in the methodology have been discussed in previous sections, notably through various sensitivity tests on deposition, boundary layer height and the turbulence diffusivity coefficient. Results have shown that all investigated uncertainties in the model influence mean EEFs, as well as their variability. They have therefore a systematic and a random part, which we could estimate in the previous sensitivity tests. These tests have shown a significant day-to-day variability, which suggests that these uncertainties are probably partly random and may thus explain most of the day-to-day variability obtained in the first results (Sect. 4.3.2). It appears to be rather tricky (and uncertain) to explain all discrepancies between individual flight results on a quantitative basis, notably due to the fact that several uncertainty sources are potentially combined. In spite of that, the choice is made to replace the uncertainty obtained in Sect. 4.3.2 by a combination of all the systematic uncertainties estimated in the previous subsection. Results of individual and the derived overall systematic uncertainty are reported in Table 3, as well as final confidence intervals on our estimation of EEFs.
Confidence intervals (at a 95 % confidence interval) on average emission
error biases deduced from the overall uncertainty are reported in Table 4.
For NO
Systematic 2
Confidence intervals on average emission error biases for the three inventories.
Also, neither the positive bias (around
To our knowledge, an evaluation of BC emissions as presented here has not yet
been attempted at the scale of a large megacity, and uncertainties estimated
at the global or regional scale are difficult to extrapolate to an
agglomeration. For comparison, through their adjoint inverse modeling
exercise over Asia, Hakami et al. (2005) found quite consistent total
assimilated and base case BC emissions over Asia, but they underlined higher
discrepancies at regional scale, with major errors over Japan and northern and
southern China of about a factor
Results obtained at ground level in Paris show a high overestimation of the
BC
Additionally, the previous tracer experiment takes into account neither the
sub-grid emissions heterogeneity at a resolution of 3
This would therefore suggest that the best BC
Black carbon (BC) emissions are still highly uncertain, and very few studies
have attempted to evaluate their inventories. This paper presents an original
approach, based on airborne measurements across the Paris plume, developed in
order to evaluate BC and NO
Various uncertainty sources in the methodology are investigated through
sensitivity tests – wind field errors, boundary layer height, vertical
mixing, deposition, and BC nature (equivalent BC vs. elemental carbon) – and
are likely to explain this variability. Results of these tests are used to
derive a systematic uncertainty between 35 and 48 % in EEFs. This
suggests that a moderate overestimation of NO
Finally, best estimations of BC and NO
To our knowledge, this study is one of the most comprehensive ones to evaluate BC emissions at the scale of a megacity. The comparison of aircraft- and ground-based results has given an interesting insight into the potential error compensation in the spatial allocation of BC emissions over a large agglomeration. In the framework of the PRIMEQUAL PREQUALIF project, a dense BC network of 14 stations (of various typologies, e.g., rural, urban, traffic) has been installed over the Paris region. It will allow for a better characterization of the BC spatial distribution over the agglomeration, and in the line with this, an interesting prospect would thus be to compare it to the simulated spatial distribution constrained by emission inventories.
The research leading to these results received funding from the European Union's Seventh Framework Programme FP/2007-2011 under grant agreement no. 212520. The authors also acknowledge the ANR through the MEGAPOLI PARIS project and ADEME and LEFE through the MEGAPOLI France project for their financial support. This work is funded by a PhD DIM (domaine d'intérêt majeur) grant from the Île-de-France region. We would like to thank the two anonymous referees for their valuable comments on this work. Edited by: A. Baklanov