Quantification of greenhouse gas emissions is receiving a
lot of attention because of its relevance for climate mitigation.
Complementary to official reported bottom-up emission inventories,
quantification can be done with an inverse modelling framework, combining
atmospheric transport models, prior gridded emission inventories and a
network of atmospheric observations to optimize the emission inventories. An
important aspect of such a method is a correct quantification of the
uncertainties in all aspects of the modelling framework. The uncertainties
in gridded emission inventories are, however, not systematically analysed.
In this work, a statistically coherent method is used to quantify the
uncertainties in a high-resolution gridded emission inventory of CO2
and CO for Europe. We perform a range of Monte Carlo simulations to
determine the effect of uncertainties in different inventory components,
including the spatial and temporal distribution, on the uncertainty in total
emissions and the resulting atmospheric mixing ratios. We find that the
uncertainties in the total emissions for the selected domain are 1 % for
CO2 and 6 % for CO. Introducing spatial disaggregation causes a
significant increase in the uncertainty of up to 40 % for CO2
and 70 % for CO for specific grid cells. Using gridded uncertainties, specific
regions can be defined that have the largest uncertainty in emissions and
are thus an interesting target for inverse modellers. However, the largest
sectors are usually the best-constrained ones (low relative uncertainty), so
the absolute uncertainty is the best indicator for this. With this knowledge,
areas can be identified that are most sensitive to the largest emission
uncertainties, which supports network design.
Introduction
Carbon dioxide (CO2) is the most abundant greenhouse gas and is emitted
in large quantities from human activities, especially from the burning of
fossil fuels (Berner, 2003). A reliable inventory of fossil fuel
CO2 (FFCO2) emissions is important to increase our understanding
of the carbon cycle and how the global climate will develop in the future.
The impact of CO2 emissions is visible on a global scale and
international efforts are required to mitigate climate change, but cities
are the largest contributors to FFCO2 emissions (about 70 %,
IEA, 2008). Therefore, emissions should be studied at
different spatial and temporal scales to get a full understanding of their
variability and mitigation potential.
One way of describing emissions is an emission inventory, which is a
structured set of emission data, distinguishing different pollutants and
source categories. Often, emission inventories are based on reported
emission data, for example, from the National Inventory Reports (NIRs)
(UNFCCC, 2019), which are national, yearly emissions based on
energy statistics. These country-level emissions can be spatially and
temporally disaggregated (scaled-down) to a certain level using proxies
(e.g. the inventories of the Netherlands Organisation for Applied Scientific
Research (TNO); Denier van der Gon et al.,
2017; Kuenen et al., 2014). Other emission inventories are based on local
energy consumption data and reported emissions, which are (dis)aggregated to
the required spatial scale (e.g. Hestia, Gurney et al., 2011,
2019) or rely on (global) statistical data and a consistent set of
(non-country-specific) emission factors representing different technology
levels, e.g. Emissions Database for Global Atmospheric Research (EDGAR) (http://edgar.jrc.ec.europa.eu, last
access: 6 May 2019). Most inventories,
including the one used in this study, rely on a combination of methods,
using large-scale data supplemented with local data. Gridded emission
inventories are essential as input for atmospheric transport models to
facilitate comparison with observations of CO2 concentrations, as well
as in inverse modelling as a prior estimate of the emission locations and
magnitude.
During the compilation of an emission inventory, uncertainties are introduced
at different levels (e.g. magnitude, timing or locations), and increasingly
more attention is given to this topic. Parties of the United Nations
Framework Convention on Climate Change (UNFCCC) report their annual
emissions (disaggregated over source sectors and fuel types) in a NIR
(UNFCCC, 2019), which includes an assessment of the
uncertainties in the underlying data and an analysis of the uncertainties in
the total emissions following IPCC (Intergovernmental Panel on Climate
Change) guidelines. The simplest uncertainty analysis is based on simple
equations for combining uncertainties from different sources (Tier 1
approach). A more advanced approach is a Monte Carlo simulation, which
allows for non-normal uncertainty distributions (Tier 2 approach). The
Tier 2 approach has been used by several countries, for example, Finland
(Monni et al., 2004) and Denmark
(Fauser et al., 2011).
These reports provide a good first step in quantifying emission
uncertainties, but the uncertainty introduced by using proxies for spatial
and temporal disaggregation is not considered. These are, however, an
important source of uncertainty in the gridded emission inventories
(Andres et al., 2016). Inverse modelling
studies are increasingly focusing on urban areas, the main source areas of
FFCO2 emissions, for which emission inventories with a high
spatiotemporal resolution are used to better represent the variability in
local emissions affecting local concentration measurements. Understanding
the uncertainty at higher resolution than the country level is thus
necessary, which means that the uncertainty caused by spatiotemporal
disaggregation becomes important as well.
The uncertainties in emission inventories are important to understand for
several reasons. First, knowledge of uncertainties helps to pinpoint
emission sources or areas that require more scrutiny
(Monni et al., 2004; Palmer et
al., 2018). Second, knowledge of uncertainties in prior emission estimates
is an important part of inverse modelling frameworks, which can be used for
emission verification and in support of decision-making
(Andres et al., 2014). For example,
if uncertainties are not properly considered, there is a risk that the
uncertainty range does not contain the actual emission value. In contrast,
if uncertainties are overestimated, the initial emission inventory gives
little information about the actual emissions and more independent
observations are needed. Third, local inverse modelling studies often rely
on daytime (12:00–16:00 LT) observations, which are easier to simulate. Given the
small size of the urban domain, these observations only contain information
on recent emissions, which have to be extrapolated using temporal profiles
to calculate annual emissions. Therefore, knowledge of uncertainties in
temporal profiles helps to better quantify the uncertainty in these annual
emissions. Finally, emission uncertainties can support atmospheric
observation system design, for example, for inverse modelling studies. An
ensemble of model runs can represent the spread in atmospheric concentration
fields due to the uncertainty in emissions. Locations with a large spread in
atmospheric concentrations are most sensitive to uncertainties in the
emission inventory and are preferential locations for additional atmospheric
measurements. To conclude, emission uncertainties are a critical part of
emission verification systems and require more attention. To better
understand how uncertainties in underlying data affect the overall
uncertainty in gridded emissions, a family of 10 emission inventories is
compiled within the CO2 Human Emissions (CHE) project, which is funded
by the Horizon 2020 EU Research and Innovation programme (see data
availability). The methodology used to create this family of emission
inventories also forms the basis for the work described here.
In this paper, we illustrate a statistically coherent method to assess the
uncertainties in a high-resolution emission inventory, including
uncertainties resulting from spatiotemporal disaggregation. For this
purpose, we use a Monte Carlo simulation to propagate uncertainties in
underlying parameters into the total uncertainty in emissions (like the
Tier 2 approach). We illustrate our methodology using a new high-resolution
emission inventory for a European region centred over the Netherlands and
Germany (Table 1, Fig. 1). We illustrate the magnitude of the uncertainties
in emissions and how this affects simulated concentrations. The research
questions are as follows:
How large are uncertainties in total inventory emissions, and how does this
differ per sector and country?
How do uncertainties in spatial proxy maps affect local measurements?
How important is the uncertainty in temporal profiles for the calculation of
annual emissions from daytime (12:00–16:00 LT) emissions, which result from urban
inverse modelling studies using only daytime observations?
What information can we gain from high-resolution gridded uncertainty maps
by comparing different regions?
Total emissions of CO2 and CO, road traffic (gasoline)
emissions of CO2 and other stationary combustion emissions of CO for 2015 in kt yr-1 (defined per grid cell).
Inverse modelling studies often focus on a single species like CO2, but
co-emitted species are increasingly included to allow source apportionment
(Boschetti et al., 2018; Zheng et al., 2019).
In this study, we look into CO2 and CO to illustrate our methodology,
but the methodology can be applied to other (co-emitted) species.
Characteristics of the high-resolution emission inventory TNO GHGco
v1.1 containing fossil fuel (FF) and biofuel (BF) emissions.
Air pollutantsFFCO, BFCO, NOxGreenhouse gasesFFCO2, BFCO2, CH4Resolution1/60∘ longitude ×1/120∘ latitude(∼1×1 km over central Europe)Period covered2015 (annual emissions)Domain47–56∘ N, -2∘ W–19∘ ESector aggregationGNFR (A to L), with GNFR F(road transport) split in F1 to F4(total 16 sectors)CountriesComplete: Germany, theNetherlands, Belgium,Luxembourg, Czech Republic; partially: United Kingdom, France, Denmark, Austria, Poland,Switzerland, Italy, Slovakiaand HungaryMethodologyThe high-resolution emission inventory
The basis of this study is a high-resolution emission inventory for the
greenhouse gases CO2 and CH4 and the co-emitted tracers CO and
NOx for the year 2015 (TNO GHGco v1.0; see details in
Table 1). In this paper, we only use CO2 and CO,
which are divided into fossil fuel (FF) and biofuel (BF) emissions (no land
use and land use change emissions are included). The emission inventory
covers a domain over Europe, including Germany, Netherlands, Belgium,
Luxembourg and the Czech Republic, and parts of Great Britain, France,
Denmark, Austria and Poland (see also Fig. 1).
The emission inventory is based on the reported emissions by European
countries to the UNFCCC (only greenhouse gases) and to EMEP/CEIP (European
Monitoring and Evaluation Programme/Centre on Emission Inventories and
Projections, only air pollutants). UNFCCC CO2 emissions have been
aggregated to ∼250 different combinations of Nomenclature For Reporting (NFR) sectors
and fuel types. EMEP/CEIP CO emissions have
been split over the same NFR sector–fuel type combinations by TNO using the
GAINS model (Amann et al., 2011) and/or
TNO data. In some cases, the reported data were gap filled or replaced with
emissions from the GAINS model, EDGAR inventory or internal TNO estimates to
obtain a consistent dataset. Next, each NFR sector is linked to a
high-resolution proxy map (e.g. population density for residential
combustion of fossil fuels or AIS (Automatic Identification System) data for
shipping regridded to 1/60∘×1/120∘), which is used
to spatially disaggregate the reported country-level emissions. Where
possible, the exact location and reported emission of large point sources are
used (e.g. from the European Pollutant Release and Transfer
Register; E-PRTR). The third step is temporal disaggregation, for which standard
temporal profiles are used (Denier van der Gon et al., 2011).
Finally, the emissions are aggregated to GNFR (gridded NFR) sectors (see
Table 2) for the emission inventory. The final
emission maps of CO2 and CO are shown in Fig. 1, together with
two examples of a source sector map. Note that these maps
do not clearly show the large point source emissions, while these make up
almost 45 % of all CO2 emissions and 26 % of all CO emissions.
Overview of aggregated NFR (GNFR) sectors distinguished in the
emission inventory.
GNFR categoryGNFR category nameAA_PublicPowerBB_IndustryCC_OtherStationaryCombDD_FugitivesEE_SolventsFF_RoadTransportF1F_RoadTransport_exhaust_gasolineF2F_RoadTransport_exhaust_dieselF3F_RoadTransport_exhaust_LPG_gasF4F_RoadTransport_non-exhaustGG_ShippingHH_AviationII_OffRoadJJ_WasteKK_AgriLivestockLL_AgriOtherUncertainties in parameters
The emission inventory is used as basis for an uncertainty analysis by
assigning an uncertainty to each parameter underlying the UNFCCC-EMEP/CEIP
emission inventories and further disaggregation thereof. Although the
aggregation to GNFR sectors makes the emission inventory more
comprehensible, we use the more detailed underlying data for the uncertainty
analysis. The reason is that the uncertainties can vary enormously between
subsectors and fuel types. Generally, the emission at a certain time and
place is determined by four types of parameters: activity data, emission
factor, spatial distribution and temporal profile. The activity data and
emission factors are used by countries to calculate their emissions. The
spatial proxy maps and temporal profiles are used for spatiotemporal
disaggregation. All uncertainties need to be specified per NFR sector–fuel
type combination that is part of the Monte Carlo simulation. In the
following sections, the steps taken to arrive at a covariance matrix for the
Monte Carlo simulation are described. Tables with uncertainty data can be
found in Appendix A.
Parameter selection
The first step is to identify which parameters should be included in the
Monte Carlo simulation. As mentioned before, there are about 250 different
combinations of NFR sectors and fuel types, and including all of them would
be a huge computational challenge. However, a selection of 112 combinations
makes up most of the fossil fuel emissions (96 % for CO2 and 92 %
for CO), and therefore a preselection was made. This results in a covariance
matrix of 224×224 parameters (112 sector–fuel combinations for two species).
To further reduce the size of the problem, the emissions are partly
aggregated before starting the Monte Carlo simulation for the spatial proxies (mostly
fuels are combined per sector, because they have the same spatial
distribution). This results in a total of 59 NFR sector–spatial proxy
combinations, which are put in a separate covariance matrix. The temporal
profiles are applied to the aggregated GNFR sectors, which make up the last
covariance matrix. Note that the spatial proxies and temporal profiles are
the same for CO2 and CO. Only the spatially explicit E-PRTR point
source data can have a different spatial distribution for CO2 and CO,
but they also use the same temporal profiles.
Uncertainties in reported emissions
Country-level emissions are estimated from the multiplication of activity
data and emission factors. Activity data consist for the most part of fossil
fuel consumption data available from national energy balances. Some fuel
consumptions are better known than others and uncertainties vary across
sectors. An emission factor is the amount of emission that is produced per
unit of activity (e.g. amount of fuel consumed). For CO2, this depends
mainly on the carbon content of the fuel. In contrast, CO emissions are
extremely dependent on combustion conditions, choice of industrial processes
and in-place technologies.
The NIRs for greenhouse gases (GHGs) provide a table with uncertainties in
activity data and CO2 emission factors on the level of
NFR sector–fuel combinations. The uncertainties reported by each country are averaged
to get one uncertainty per NFR sector–fuel combination for the entire
domain. Overall, the differences in reported uncertainties between countries
are small. The uncertainties in activity data and CO2 emission factors
are relatively low and normally distributed.
The CO emission factors are mostly based on uncertainty ranges provided in
the EMEP/EEA Guidebook (European Environment Agency, 2016) and
supplemented by BAT reference documents from which reported emission factor
ranges are taken as uncertainty range
(http://eippcb.jrc.ec.europa.eu/reference/, last access: 24 January 2019). The CO emission factor
uncertainties are generally expressed by a factor, which means that the
highest and lowest limit values are either the specified factor above or
below the most common value. Therefore, these uncertainties have a lognormal
distribution and are relatively large.
To estimate the overall uncertainty in the emissions per NFR sector–fuel
combination, the uncertainties in the activity data and emission factors
need to be combined (shown in Fig. 2 for the
aggregated GNFR sectors). When both uncertainties are of the same order and
relatively small, as well as both having a normal distribution, the overall
emission uncertainty is calculated with the standard formula for error
propagation for non-correlated normally distributed variables (see Sect. 2.4).
For most CO emission factors, uncertainties are much higher and have a
lognormal distribution instead of normal. In that case, the uncertainty of
the variable with the highest uncertainty is assumed to be indicative of
the overall uncertainty of the emission, which in general means the
uncertainty of the CO emission factor determines the overall uncertainty of
the CO emission, with the distribution remaining lognormal. The error
introduced by fuel type disaggregation for CO is not considered.
Covariance matrices for total emissions of CO2(a) and CO
(b) per aggregated source sector. A white space on the diagonal
indicates this sector is not included in the Monte Carlo simulation.
Finally, for power plants and road traffic, we assumed error correlations to
exist between different subsectors per fuel type and between different
fuel types per subsector for other NFR sectors. In some cases, correlations
also exist between different NFR sectors belonging to the same GNFR sector.
The definition of correlations is important, because they affect the total
uncertainties. For example, if emission factors of subsectors are
correlated, deviations can amplify each other, leading to higher overall
uncertainties. In contrast, the division of the well-known total fuel
consumption of a sector over its subsectors includes an uncertainty which
is anti-correlated (i.e. if too much fuel consumption is assigned to one
subsector, too little is assigned to another). This has little impact on
the total emissions, because uncertainties only exist at lower levels.
Uncertainties in spatial proxies
The proxy maps used for spatial disaggregation can introduce a large
uncertainty coming from the following sources:
The proxy is not correctly representing real-world locations of what it is
supposed to represent, either because there are cells included in which none
of the intended activity takes place or cells are missing in which the
intended activity does take place (proxy quality).
The proxy is not fully representative of the activity it is assumed to
represent, for example, if there is a non-linear relationship between the
proxy value and the emission (proxy representativeness): a grid cell with
twice the population density does not necessarily have double the amount of
residential heating emissions, because heating can be more efficient in
densely populated areas and/or apartment blocks.
The cell values themselves are uncertain, e.g. the population density or
traffic intensity (proxy value).
We attempt to capture the second and third source of uncertainty in a single
numerical indicator representing the uncertainty at cell level (see
Fig. 3 for the uncertainty per aggregated GNFR
sector). The overall uncertainties are based on expert judgement and
inevitably include a considerable amount of subjectivity. This type of
uncertainty is often large and has a lognormal distribution, except for
proxies related to road traffic and some proxies related to
commercial/residential emissions sources. We assume no error correlations
exist. The first source of uncertainty is also considered in one of the
experiments (see Sect. 2.4 for a description of this experiment).
Uncertainties in temporal profiles
For each GNFR sector, the emission timing is described using three temporal
profiles: one profile that describes the seasonal cycle (monthly fractions),
one profile that describes the day-to-day variations within a week (daily
fractions) and one profile that describes the diurnal cycle (hourly
fractions). These profiles are based on long-term average activity data
and/or socioeconomic characteristics and are applied for each year and for
the entire domain, considering only time zone differences. In reality, the
temporal profiles can differ between countries, from year to year, and the
diurnal cycle can vary between weekdays and weekends. For example,
residential emissions are strongly correlated with the outside temperature,
which follows a different pattern each year.
To quantify the uncertainty in temporal profiles, a range of temporal
profiles (for a full year, hourly resolution) was created for each source
sector based on activity data (such as traffic counts). These profiles can
be from different years and countries, so that the full range of
possibilities is included. These are compared to the fixed temporal profiles
to estimate the uncertainties, which are normally distributed (see
Fig. 3 for the uncertainty per aggregated GNFR
sector). We assume no error correlations exist.
Covariance matrices for spatial proxies (a) and time profiles
(b) per aggregated source sector. These are the same for CO2 and
CO. A white space on the diagonal indicates this sector is not included in
the Monte Carlo simulation.
The Monte Carlo simulation
Within a Monte Carlo simulation, we create an ensemble (size N) of emissions,
spatial proxies and temporal profiles by drawing random samples from the
covariance matrices described in Sect. 2.2. This creates a set of possible
solutions in the emission space, reflecting the uncertainties in the
underlying parameters. The entire process is shown in
Fig. 4. As mentioned before, not all subsectors
are included in the Monte Carlo simulation and the non-included emissions
are added to each ensemble member at the final stage. It is important to
ensure that the temporal profiles and the spatial proxies do not affect the
total emissions, so proxies should sum up to 1 for each country and temporal
profiles should be on average 1 over a full year. Before doing this,
negative values are removed.
Flow diagram showing the input, processing and output of the Monte
Carlo simulation.
The source sectors that include point source emissions (mainly public power
and industry) are treated separately. The large point source emissions and
their locations are relatively well known and available from databases (e.g.
from E-PRTR) and therefore not included in the Monte Carlo simulation. The remaining
part of the emissions (non-point source or small point sources) from these
sectors is distributed using generic proxies (e.g. industrial areas) and
is calculated as the difference between the total emissions
(activity data multiplied by the emission factor) and the sum of the point source emissions. If negative
emissions result from this subtraction of reported point source emissions,
the residual is set to zero. Note that the spatial uncertainty of this
residual part is often high. The fractions of the public power and industrial
emissions that are attributed to large point sources are shown in
Table 3 for several countries.
Percentage (%) of emissions of CO2 and CO (FF plus BF) that
are attributed to large point sources (accounted for in databases) for
public power and industry source sectors.
In this paper, several experiments are performed to examine the impact of the
uncertainties in different parameters on the overall emissions and simulated
concentrations:
The first experiment uses a Monte Carlo simulation (N=500) to illustrate
the spread in emissions per sector due to uncertainties in emission factors
and activity data (no spatial/temporal variability is considered). This
sample size is based on an analysis of the robustness of the uncertainty
estimate (Janssen, 2013), which shows that a
sample size of 500 is sufficient to get robust results (Appendix B). This
experiment is used to show the contribution of specific sectors to the
overall uncertainty and to illustrate how uncertainties vary between sectors
and countries. For this experiment, country totals are used, also for the
countries that are partially outside the zoom domain shown in
Fig. 1. The results are presented in Sect. 3.1.
The second experiment uses a Monte Carlo simulation (N=500) to illustrate
how the uncertainty in spatial proxy maps is translated into uncertainties
in simulated concentrations (emissions are taken constant; no temporal
variability is included). We use emissions of other stationary combustion
(CO2) and road traffic (CO) to illustrate the importance of having a
correct spatial distribution for measurements close to the source area and
further away. The results are presented in Sect. 3.2.
The third experiment compares two spatial proxy maps for distributing
“residual” power plant emissions (i.e. those not accounted for in point
source databases) to illustrate the potential impact of spreading out small
point source emissions when zooming in on small case study areas (emissions
are taken constant; no temporal variability is included). The results are
presented in Sect. 3.2.
The fourth experiment uses a Monte Carlo simulation (N=500) to illustrate
the spread in temporal profiles (emissions are taken constant; no spatial
variability is considered). We use this information to determine the error
introduced when extrapolating daytime (12:00–16:00 LT) emissions (for example,
resulting from an inversion) to annual emissions using an incorrect temporal
profile. Figure 5 shows two possible daily cycles,
which have 46 % (blue) and 25 % (orange) of their emissions between 12:00
and 16:00 LT. Therefore, both temporal profiles will give a different total
daily emission when used to derive the daytime emissions. The results are
presented in Sect. 3.3.
For the final experiment, maps are made of the (absolute and relative)
uncertainty in each pixel, including uncertainties in emission factors,
activity data and spatial proxies (no temporal variability). For this, we
used a Tier 1 approach, using the following equations:Relative uncertainty=∑standard deviations2/emission sumfor the summation of uncorrelated quantities (e.g. sectoral emissions)
andTotal relative uncertainty=∑relative uncertainties2for the multiplication of random variables, such as used to combine activity
data and emission factors. Here, the (total) relative uncertainty is the
percentage uncertainty (uncertainty divided by the total) and the standard
deviations are expressed in units of the uncertain quantity (percentage
uncertainty multiplied with the uncertain quantity). These maps are used to
explore spatial patterns in uncertainties and examine what we can learn
about different countries or regions. The results are presented in Sect. 3.4.
For experiments 2 and 3, a smaller domain is selected to represent a local
case study (Fig. 6). We used the Rotterdam area,
which has been studied in detail before
(Super
et al., 2017a, b). The domain is about 34×26 km and centred over the
city, which includes some major industrial activity as well. To translate
the emissions into atmospheric concentrations, a simple plume dispersion
model is used, the Operational Priority Substances (OPS) model. This model
was developed to calculate the transport of pollutants, including chemical
transformations (Van Jaarsveld, 2004; Sauter et al.,
2016) and was adapted to include CO and CO2
(Super
et al., 2017a). The short-term version of the model calculates hourly
concentrations at specific receptor points, considering hourly variations in
wind direction and other transport parameters. Although the model is often
used for point source emissions, it can also handle surface area sources.
This model was chosen because of its very short runtime, which makes it
suitable for a large ensemble. The model is run for each of the alternative
emission maps.
Schematic overview of two possible temporal profiles, which
represent a different fraction of the total daily emissions during the
selected period (12:00–16:00 LT, illustrated by the dashed lines).
Emissions of CO2(a) and CO (b) for part of the
Netherlands, including the subdomain (black rectangle) over Rotterdam.
Black stars indicate the receptor locations.
The OPS model is run for each ensemble member for 5 January 2014 from the
start of the day until 16:00 LT. On this day, the wind direction is relatively
constant at about 215∘ and the wind speed is around 6 m s-1.
We specify receptor points downwind from the centre of our domain at
increasing distance (5, 10, 15, 20, 30 and 40 km). We use the last hour of
the simulation for our analyses. We assume emissions from other stationary
combustion and road traffic (experiment 2) to take place at the surface. The
initial emissions of “residual” power plants, smeared out over all
industrial areas, are also emitted at the surface. However, we raise the
height of the emissions to 20 m when these emissions are appointed to
specific pixels. This height is representative of stack heights of small
power plants.
ResultsUncertainties in total emissions
Using the uncertainties in emission factors and activity data, we can
evaluate the uncertainty in the total emissions of CO2 and CO per
sector. Figure 7 shows the normalized spread in
emissions per sector based on the Monte Carlo simulation (N=500). The
CO2 emissions have a relatively small uncertainty range and the
uncertainty in the total emissions (if we sum all GNFR sector emissions for
each of the 500 solutions) is only about 1 % (standard deviation). The
largest uncertainties are for fugitives and aviation, which are only small
contributors to the total CO2 emissions (1.3 % and 0.4 %,
respectively). Therefore, their contribution to the total emission
uncertainty is very small, as is shown in Fig. 8.
The largest uncertainty in the total CO2 emissions is caused by the
public power sector. Despite the relatively small uncertainty in the
emissions from this sector, it is the largest contributor to the total
CO2 emissions (33 %), and therefore the uncertainty in the public
power sector contributes about 45 % to the uncertainty in the total
CO2 emissions.
Normalized spread in emissions of CO2(a) and CO (b).
The box represents the interquartile range, the whiskers the 2.5–97.5
percentiles, the lines the median values, and the circles are outliers. For
sectors where no box is drawn, there are no data included in the Monte Carlo
simulation. Note the different scales of the y axis.
Contribution of source sectors to the total uncertainty in
CO2(a) and CO emissions (b), summing to 100 %.
In contrast, the CO emissions show a larger uncertainty bandwidth, with many
high outliers caused by the lognormal distribution of uncertainties in the
emission factors. The uncertainty in the total emissions is 6 % for CO
(standard deviation). Here, again, the largest uncertainties are related to
sectors (public power and road transport (LPG fuel)) that are relatively
small contributors to the total CO emissions. The main contributor to the
uncertainty in total CO emissions is other stationary combustion, which
contributes about 31 % to the total emissions and is responsible for more
than 60 % of the total uncertainty.
Although the uncertainty in each parameter is assumed to be the same for
each country, how a sector is composed of subsectors can vary per country.
Therefore, the uncertainty per aggregated sector can also vary per country.
An example is shown in Fig. 9a, which
shows the normalized spread in CO2 emissions of other stationary
combustion for all countries within the domain. We find a much larger
uncertainty in countries where the relative fraction of biomass combustion
is larger, because biomass burning has a much larger uncertainty in both the
activity data and the emission factor. For example, the percentage of
biomass burning in the residential sector is 54 % for the Czech Republic
and 65 % for Denmark, compared to only 11 % and 9 % for the
Netherlands and Great Britain. Thus, differences in the fuel composition of
countries result in differences in the overall emission uncertainties, even
if the uncertainty per parameter is estimated to be the same. For the total
CO2 emissions, the differences between countries are small, with
standard deviations between 1.2 % and 2.3 % (Fig. 9b).
Normalized spread in emissions of CO2 for other stationary
combustion (a) and all sectors combined (b) for a range of countries.
The box represents the interquartile range, the whiskers the 2.5–97.5
percentiles, the lines the median values, and the circles are outliers.
Uncertainties in spatial proxies
We examined the impact of uncertainties in spatial proxies on modelled
CO2 and CO concentrations for major source sectors. For CO2, we
selected other stationary combustion (only commercial/residential; no
agriculture/forestry/fishing). The largest fraction (> 90 %)
of CO2 emissions from this sector is distributed using population
density as proxy, which is used here (the remainder of the emissions is not
considered). The uncertainty in this sector–proxy combination is estimated
to be 50 % (normal distribution), mainly due to the disaggregation to the
1×1 km resolution. For CO, we selected road transport (all fuels but only
passenger cars). The spatial proxy for distributing passenger car emissions
is based on traffic intensities compiled using Open Transport Map and Open
Street Map, vehicle emission factors per road type/vehicle type/country and
fleet composition. The uncertainty in this proxy is estimated to be 30 %
(normal distribution) due to a higher intrinsic resolution.
Spread in simulated concentrations of CO2 resulting from
commercial/residential emissions due to uncertainties in the total
population proxy map (a) and spread in concentrations of CO resulting
from road transport (passenger cars) emissions due to uncertainties in the
passenger cars proxy map (b). The box represents the interquartile
range, the whiskers the 2.5–97.5 percentiles, and the lines the median
values of the full ensemble.
Figure 10 shows the resulting spread in atmospheric
concentrations as a function of downwind distance from the source area. Note
that the concentrations are enhancements caused by local emissions of the
selected source sectors and do not include ambient concentrations or other
sources. For CO2 (Fig. 10a), we see a concentration of about 3.0 ppm
at 10 km from the source area centre but with a large uncertainty
bandwidth. This signal is large enough to measure, but with this large
uncertainty such measurements are difficult to use in an inversion. The
measurement at 5 km from the source area centre is slightly lower than the
one at 10 km, because it is upwind of a part of the emissions. At longer
distances, the concentration enhancement decreases drastically and so does
the absolute spread in concentrations. The enhancement becomes too small
compared to the uncertainties occurring in a regular inversion framework to
be useful. Figure 10b shows a similar picture for the CO concentrations
resulting from passenger car emissions. Again, the spread in concentrations
is large close to the source area centre and decreases with distance, but
also the absolute concentration enhancement decreases. However, in this case,
the concentration at 5 km from the source area centre is larger, because it
is very close to an emission hotspot (see also Fig. 6). Note that we focus
here on a single source sector and the overall enhancements will be larger
and therefore easier to use. Nevertheless, the large spread in
concentrations shows that a good representation of the spatial distribution
is important for constraining sectoral emissions.
Both proxy maps discussed here are the main proxy maps for the selected
sectors. As mentioned before, some sectors have residual emissions that are
distributed using an alternative proxy map. An example is public power.
Large power plants are listed in databases, including the reported emissions
(Table 3). The remainder of the country emissions is spatially distributed
over all industrial areas. However, it is more likely that the residual
emissions should be attributed to specific point sources (small power plants
not listed in databases). That means that instead of spreading the emissions
over a large area, leading to very small local emissions and a low
concentration gradient, there could be relatively large emissions at a few
locations. Therefore, the uncertainty in these sector–proxy combinations is
assumed to have a lognormal distribution, in part because of the absence of
a better estimation.
We illustrate the effect of this assumption by creating a new proxy map for
residual (small) power plants. We find that for the Netherlands a total
capacity of 3655 MWe by 676 combustion plants is not included as a point
source (source: S&P Global Platts World Electric Power Plants database
(https://www.spglobal.com/platts/en/products-services/electric-power/world-electric-power-plants-database,
last access: 25 April 2019)).
At least 70 % of this capacity, attributed to 280 plants, is assumed to
be in industrial areas. Given 4052 grid cells designated as industrial area
in the Netherlands, this is just 7 % of the total amount of industrial
area grid cells assuming no more than one plant per grid cell. The remainder
is mainly related to cogeneration plants from glasshouses, which are located
outside the industrial areas. Therefore, we create a new proxy map for power
plants by equally assigning 70 % of the emissions from the residual power
plants to 20 randomly chosen pixels (7 % of the total amount of
industrial area pixels in the case study area, i.e. the same density as for
the Netherlands as a whole). As mentioned before, we also raise the height
of the emissions from surface level to 20 m, which is a better estimate of
the stack height for small power plants.
The effect on local measurements is large (Fig. 11). Instead of
measuring a small and constant signal from this sector, the
assumed presence of small power plants results in measuring occasional large
peak concentrations. Thus, despite being relatively unimportant at the
national level, for local studies, the impact of the uncertainty in these
“residual” proxies can be large.
Spread in simulated concentrations of CO2 resulting from
public power emissions due to differences in the proxy map: emissions are
distributed using the new proxy map with only 20 randomly chosen pixels
containing emissions. The box represents the interquartile range, the
whiskers the 2.5–97.5 percentiles, the lines the median values, and the
black circles are outliers of the full ensemble. The red dots show
concentrations of CO2 when the original proxy map is used (industrial
area).
Uncertainties in temporal profiles
The timing of emissions is important to interpret measurements correctly.
During morning rush hour, a peak is expected in road traffic emissions, but
the magnitude of this peak can differ from one day to the next. Also, the
seasonal cycle in emissions due to heating of buildings can vary between
years due to varying weather conditions. Yet, often fixed temporal profiles
are used to describe the temporal disaggregation of annual emissions. The
range of possible values for the temporal profile of other stationary
combustion is shown in Fig. 12. The range can be
very large, especially during the winter. However, note that the average of
each temporal profile is 1.0 for a full year, so that the temporally
distributed emissions add up to the annual total. Therefore, changes in the
temporal profile indicate shifts in the timing in the emissions and not
changes in the overall emissions due to cold weather, which are accounted
for by the activity data.
Spread in temporal profiles for other stationary combustion
(N=500), resulting from the Monte Carlo simulation (grey shading). The
black line represents the standard time profile.
In inverse modelling, often well-mixed (non-stable) daytime measurements are
selected
(Boon
et al., 2016; Breón et al., 2015; Lauvaux et al., 2013), because these
are least prone to errors in model transport. For local studies, where
transport times are short, this means that only afternoon emissions are
optimized. The total annual emissions can then be calculated using a
temporal profile. However, if the temporal profile is not correct, an
incorrect fraction of the emissions can be attributed to the selected hours.
We examined the impact of using an incorrect temporal profile on the total
yearly emissions by calculating yearly emissions for each ensemble member.
Figure 13 shows the normalized spread in sectoral
emissions for all ensemble members. The error in the total annual emissions,
resulting from the upscaling of daytime emissions using an incorrect
temporal profile, can reach up to about 1 %–2 %. This is a significant
source of error for country-level CO2 emissions but less important for
CO, as the other uncertainties for CO are much larger.
Normalized spread in emissions of CO2 and CO per sector due
to uncertainties in temporal profiles. The box represents the interquartile
range, the whiskers the 2.5–97.5 percentiles, the lines the median values,
and the circles are outliers. The spread is the same for CO2 and CO,
because they have the same temporal profiles.
Uncertainty maps and spatial patterns
As mentioned before, the uncertainty of the emission value in a grid cell is
determined by the uncertainties in activity data, emission factors and
spatial distribution proxies. The gridded uncertainty maps in
Figs. 14 and 15
illustrate that countries or (types of) regions differ significantly in
their emission uncertainty, both in absolute and relative values. Concerning
the uncertainty in CO2 and CO emissions, several observations can be
made.
Maps of the relative and absolute uncertainty in CO2
emissions. Areas that are examined in more detail are outlined by black
squares in the top panel.
Maps of the relative and absolute uncertainty in CO emissions.
Areas that are examined in more detail are outlined by black squares in the
top panel.
First, for both CO and CO2, the road network is visible due to low
relative uncertainties and high absolute uncertainties compared to the
surroundings. This indicates that, despite having large emissions per pixel,
the spread in road traffic emissions among ensemble members is relatively
small. This is likely due to the small (normally distributed) uncertainty in
the spatial proxies for road traffic; i.e. the location of the roads is
well known. The surrounding rural areas are dominated by other stationary
combustion, which has a slightly larger spatial uncertainty.
Second, in Austria (Tirol mainly), a large area of high relative uncertainty
in CO2 emissions is visible (average pixel emission is 220 t CO2 yr-1), which we compare to an area just on the other side of
the border in southern Germany (average pixel emission is 495 t CO2 yr-1). The uncertainty in both areas is dominated by other
stationary combustion. Yet, in Austria, a lot of biofuel is used (52 % of
the total emissions for this source sector) with a large uncertainty in the
emission factor and spatial distribution, whereas in Germany only 20 % of
the emissions in this sector are caused by biofuel combustion. On the other
hand, the absolute uncertainty is very small in Tirol because of the low
population density (and thus small emissions) in this mountainous area.
Third, some large patches of high relative uncertainty in CO2 emissions
are visible in the Czech Republic and the northeast of France. The location
of these patches seems to correspond to natural areas/parks. Therefore,
absolute uncertainties are low in these areas given the low emissions
(average pixel emission in the Sumava national park is 22 t CO2 yr-1). The total uncertainty can be explained for 60 % by the
uncertainty in other stationary combustion, mainly wood burning
(Fig. 16). Also, agriculture (field burning of
residues) plays a significant role. In addition to these natural areas,
there are also some very small dark red areas (relative uncertainty) in
northern France. These areas are military domain and have a lower absolute
uncertainty than their surroundings because very few emissions are
distributed to these areas (average pixel emission is 250 t CO2 yr-1). The public power and industrial emissions are probably too small
to be reported, hence the large relatively uncertainty.
Contribution of source sectors to the total emissions (a) and
the total uncertainty (b) in CO2 for the Sumava national park in
the Czech Republic and a hotspot in France, summing to 100 %. See
Fig. 14 for the exact locations of these areas.
Fourth, strongly urbanized areas like Paris, the Ruhr area in Germany and
Rotterdam (also see Fig. 1 for their locations) are clearly visible as areas
where the relative uncertainty in CO emissions is lower than in the
surrounding areas. Compared to its surroundings, the uncertainty in Paris is
mainly determined by the industrial sector (Fig. 17). Since industrial emissions are relatively well known, the relative
uncertainty is small. However, the absolute uncertainty is large for big
cities because of the high emissions in these densely populated areas
(average pixel emission is 64 t CO yr-1 for Paris). In the
surrounding areas, the emissions are again dominated by other stationary
combustion, which has a larger uncertainty. Yet, the absolute uncertainty is
smaller because of the lower emissions (average pixel emission is 12 t CO yr-1).
Contribution of source sectors to the total emissions (a) and
the total uncertainty (b) in CO for Paris and its surroundings, summing
to 100 %. See Fig. 15 for the exact locations of
these areas.
Finally, the relative uncertainties seem to be consistently higher in some
countries than in others. For example, the relative uncertainty in the total
emissions of France and Great Britain (only pixels within the domain) are
39 % and 25 %, respectively. For France, the main sources of uncertainty
are industry and other stationary combustion, whereas the off-road and road
transport sectors have a significant contribution to the uncertainty in
Great Britain (Fig. 18). The main difference
between the countries is again the amount of biomass used in the other
stationary combustion sector (26 % in France and 8 % in Great
Britain). This is likely to explain why in rural areas the relative
uncertainty is much higher in France.
Contribution of source sectors to the total emissions (a) and
the total uncertainty (b) in CO for France and Great Britain, summing to
100 %.
Discussion
Several previous studies have examined the uncertainty in emissions, either
globally or nationally. For example, Andres et al. (2014) studied the uncertainty in
the Carbon Dioxide Information Analysis Center (CDIAC) emission inventory on a global scale, suggesting that the largest
uncertainties are related to the fuel consumption (i.e. activity data). A
similar concern was identified for China, for which the uncertainty in
energy statistics resulted in an uncertainty ratio of 15.6 % in the 2012
CO2 emissions (Hong et al.,
2017). In the present study, the uncertainties in activity data and emission
factors are similar for CO2, whereas the uncertainty in CO emission
factors is much larger than the uncertainty in activity data. A possible
explanation for this is that the energy statistics for the European
countries included here are more reliable than for developing countries. The
occurrence of large differences in the reliability of reported emissions
between countries is also illustrated by Andres et al. (2014). In addition
to these scientific studies, many countries report uncertainties in emission
estimates in their National Inventory Reports (UNFCCC, 2019).
Yet, their methods for uncertainty calculation differ and can even vary over
time. Several scholars have examined the uncertainty in national greenhouse
gas emissions in more detail. For example, Monni et al. (2004) (Finland)
and Fauser et al. (2011) (Denmark) used a Tier 2 approach (Monte Carlo
simulation) to determine the uncertainty in the total greenhouse gas
emissions (in CO2 equivalents). They found an uncertainty of
about 5 %–6 % for the year 2001 for Finland and an uncertainty of 4 %–5 % for the
year 2008 for Denmark, also considering non-normal distributions in
uncertainties. Moreover, Oda et al. (2019) found a 2.2 %
difference in total CO2 emissions in Poland between two emission
inventories, which is in agreement with our total CO2 emission
uncertainty.
Even fewer studies have focused on uncertainties in the proxy maps used for
spatial disaggregation. Some studies compared emission inventories to get an
idea of the spatial uncertainties
(Gately and Hutyra,
2017; Hutchins et al., 2017), but these studies are likely to underestimate
uncertainties due to systematic errors that occur when different emission
inventories use similar methods and/or proxies for spatial allocation.
Moreover, exact quantification of uncertainties is often limited, dependent
on the spatial scale, and the uncertainties are not specified per source
(i.e. total emissions and spatial disaggregation) (Oda et
al., 2019). Sowden et al. (2008) used a
qualitative approach to identify the uncertainty of different components of
their emission inventory for reactive pollutants (activity, emission
factors, spatial and temporal allocation and speciation) by giving each
component a quality rating. They suggest that spatial allocation is an
important source of uncertainty for residential burning but not so much for
point sources and road traffic. Indeed, the locations of large point sources
and roads are relatively well known. However, we consider the allocation of
emissions to pixels that include roads to have a significant (pixel value)
uncertainty. Therefore, our results show that uncertainties in the spatial
proxy used for road traffic can cause a significant spread in CO
concentrations.
Andres et al. (2016) did a more extensive
analysis of the spatial distribution in CDIAC, including uncertainties in
pixel values (e.g. due to incorrect accounting methods or changes over time)
and due to the representativeness of the proxy for the spatial distribution
of emissions (also see Sect. 2.2.3). We considered these sources of
uncertainty as well. However, Andres et al. (2016) also mention spatial discretization as
a source of error, because they assign each pixel (1×1∘
resolution) to one country. The proxy maps used in this study include
country fractions in each pixel, reducing this uncertainty. In contrast, we
suggest another source of uncertainty, namely the fact that some pixels can
include emissions while no activity takes place there, or vice versa (proxy
quality). Based on the listed uncertainties, Andres et al. (2016) found an average uncertainty (2σ) in individual pixels of 120 % (assuming normal distributions). Here,
we find an average uncertainty (2σ) of 36 %. However, a small
number of large outliers occur (less than 0.01 % of the pixels has an
uncertainty of > 1000 %) due to lognormal error distributions,
although these are related to pixels with small emissions. A large part of
the difference can be explained by the large pixel size of CDIAC and the
large error introduced by spatial discretization (e.g. due to pixels that
cover large areas of two different countries). Also, their emissions are
spatially distributed based on population density, while we use a range of
proxy maps depending on the source sector and use specific locations for
large point sources. However, the uncertainty estimates are partially based
on expert judgement and remain subjective. Moreover, the uncertainty related
to the location of actual activities is not included in our uncertainty
estimate, even though we have shown this can have a large impact locally.
The country-level CO2 emissions used for our emission inventory are
based on NIRs, which are assumed to be relatively accurate because of the
use of detailed fuel consumption statistics and country-specific emission
factors
(Andres et
al., 2014; Francey et al., 2013). The uncertainties reported in the NIRs
were determined following specified procedures and are deemed the most
complete and reliable estimates available. Yet, because of the use of
prescribed methods and in some cases general emission factors, systematic
errors can occur both in the estimate of parameters and in the estimate of
uncertainties. We choose to average the uncertainties reported by several
countries, because the uncertainty estimates are relatively consistent
across countries. However, this would not eliminate such systematic errors.
The effect of systematic errors could be analysed by comparing different
sources of information. Additionally, we assume point source emissions are
relatively certain, yet a recent study showed that significant uncertainties
exist in reported emissions of US power plants (Quick and
Marland, 2019). A similar study for Europe is recommended, not only to
improve the knowledge for the European situation but also to understand
continental differences.
One source of uncertainty that is not considered in this study is the
incompleteness of the emission inventory (i.e. if sources are missing) or
double-counting errors. For example, during the compilation of the base
inventory, we found that in several cases the CO2 emissions from
airports were very low. The reason was that emissions from international
flights are not reported in the NIRs and are therefore not part of the
emission data used to create the inventory. Once discovered, this was
corrected, and aircraft landing and take-off emissions from international
flights were added in a later stage. Such discrepancies caused by reporting
guidelines could be present for other source types as well. Although overall
this error is likely to be small, locally the errors might be significant.
Finally, Sowden et al. (2008) mention
(dis)aggregation as another source of error, i.e. the calculation of
emissions on a different scale (spatially, temporally or at sector level) than
the input data. In principle, fuel consumption data are available on
aggregated levels and then separated over different subsectors. This
increases the uncertainty at the lower level, but on the aggregated level
the uncertainties remain the same. A similar note was made by Andres et
al. (2016) about the use of higher-resolution
proxy maps, which might increase the uncertainty due to lack of local data.
However, when local data are available, this might also decrease the
uncertainties. For example, the EDGAR emission database uses
non-country-specific emission factors based on technology levels and sector aggregated
energy statistics (Muntean et al., 2018). The reason is
that the level of detail we used in this paper is not available globally.
However, using generic emission factors can introduce large uncertainties
when subsectoral chances occur. Therefore, regional/local studies could
benefit from using a dedicated emission inventory for their region of
interest instead of a global inventory.
Our results can be used to support network design and inverse modelling. The
uncertainty maps are helpful to identify regions with large emission
uncertainties, which can be the focus point of an inversion with the aim to
optimize emissions in those regions. However, inverse modelling requires an
observational network that is sensitive to the emissions from the regions of
interest. A site is sensitive to specific emissions when it is often
affected by them, taking into account the dominant wind direction and the
magnitude of concentration enhancements, which should be larger than the
uncertainties that affect model–observation comparison (e.g. measurement
uncertainty and model errors). Plumes from emission hotspots can travel a
long distance, and sites up to 30 km downwind have shown to be able to detect
urban signals
(Super
et al., 2017a; Turnbull et al., 2015). The concentration enhancement in
these plumes is large and therefore easy to detect. In contrast, the
concentration enhancements of a single source (sector) are much smaller, as
shown in Figs. 10 and 11, and therefore they become undetectable at much
shorter distances. For example, vehicle exhaust emissions were shown to
decrease by a factor 2 at 200 m from a highway
(Canagaratna et al., 2010), while power
plants plumes have been detected several kilometres downwind
(Lindenmaier et al., 2014).
Dilution is strongly dependent on the atmospheric conditions, and also the
height of the measurement site plays an important role. To conclude, the
optimal network design is strongly dependent on which question needs to be
answered and the focus area and resolution needed to reach this goal.
Conclusions
In this work, we studied the uncertainties in a high-resolution gridded
emission inventory for CO2 and CO, considering uncertainties in the
underlying parameters (activity data, emission factors, spatial proxy maps
and temporal profiles). We find that all factors play a significant role in
determining the emission uncertainties, but that the contribution of each
factor differs per sector. Disaggregation of emissions introduces additional
sources of uncertainty, which makes uncertainties at a higher resolution
larger than at the scale of a country/year and can have a large impact on
(the interpretation of) local measurements. This is an important
consideration for inverse modellers, and our methodology can be used to better
define local uncertainties for, e.g. urban inversions. Inverse modellers
should be aware that the use of erroneous temporal profiles to extrapolate
emission data could result in errors of a few percent, which for CO2 is
significant. In the future, the temporal profiles could be improved by using
detailed activity data, e.g. from power plants. Moreover, we found that
large regional differences exist in absolute and relative uncertainties. By
looking in more detail at specific regions (or countries), more insight can
be gained about the emission landscape and the main causes of uncertainty.
Interestingly, areas with larger absolute uncertainties often have smaller
relative uncertainties. A likely explanation is that large sources of
CO2 and CO emissions received more attention and are therefore
relatively well constrained, for example, in the case of large point sources.
Nevertheless, since we are most interested in absolute emission reductions,
the map with absolute uncertainties can be used to define an observational
network that is able to reduce the largest absolute uncertainties. Finally,
we believe that an uncertainty product based on a well-defined,
well-documented and systematic methodology could be beneficial for the
entire modelling community and support decision-making as well. However,
specific needs can differ significantly between studies, for example, the
scale/resolution, source sector aggregation level and which species are
included. Therefore, the creation of a generic uncertainty product is
challenging and needs further research.
Relative uncertainties (fraction) in activity data and CO2
emission factors as taken from the NIRs (country average) and in CO emission
factors as derived from literature (assumed equal for all countries in the
domain). The quoted uncertainty ranges are assumed to be representative of
1 standard deviation. Uncertainties in activity data and CO2 emission
factors are often relatively low and symmetrically distributed, and normal
distributions (Norm) are assumed for these activities. Compared to CO2
emission factors, the uncertainty in CO emission factors is much higher, up
to an order of magnitude. Uncertainties in CO emission factors are often
lognormally distributed (Logn) and are assumed equal for all countries in
the HR domain. The uncertainty in the activity of open burning of waste (not
covered by the NIRs) is also assumed to have a lognormal distribution.
Sector (NFR)Fuel typeActivity data CO2 emission factors CO emission factors AverageDistributionAverageDistributionAverageDistributionPublic electricity and heat production (1.A.1.a)Solid (fossil)0.018Norm0.030Norm0.149LognLiquid (fossil)0.022Norm0.031Norm0.399NormGaseous (fossil)0.021Norm0.015Norm0.513NormBiomass0.060Norm0.05Norm0.231LognOil and gas refining (1.A.1.b & 1.B.2.d)All0.038Norm0.048Norm0.402NormOil production and gas exploration (1.B.2 mainly flaring, 1.B.2.c)All0.118Norm0.141Norm0.240LognIron and steel industry (1.A.2.a & 2.C.1)All0.044Norm0.056Norm0.240LognNon-ferrous metals (1.A.2.b & 2.C.2_3)All0.031Norm0.029Norm0.208NormChemical industry (1.A.2.c & 2.B)All0.042Norm0.041Norm0.138LognPulp and paper industry (1.A.2.d)All0.027Norm0.016Norm0.138LognFood processing, beverages and tobacco (1.A.2.e)All0.029Norm0.017Norm0.138LognNon-metallic minerals (1.A.2.f & 2.A)All0.032Norm0.041Norm0.384LognOther manufacturing industry (1.A.2.g)All0.029Norm0.014Norm0.138LognCivil aviation – LTO (1.A.3.a)All0.089Norm0.040Norm0.231LognRoad transport (all vehicle types) (1.A.3.b)Gasoline (fossil)0.031Norm0.025Norm0.284LognDiesel (fossil)0.032Norm0.026Norm0.319NormGaseous (fossil)0.039Norm0.027Norm0.320LognLPG0.039Norm0.027Norm0.462NormOther transport (1.A.3.e & 1.A.4 mobile)All0.067Norm0.023Norm0.384LognOther mobile (1.A.5.b)All0.098Norm0.026Norm0.384LognResidential (1.A.4.b)Gaseous (fossil)0.040Norm0.022Norm0.141LognLiquid (fossil)0.048Norm0.024Norm0.404NormSolid (fossil)0.085Norm0.041Norm0.141LognBiomass0.163Norm0.055Norm0.384LognCommercial institutional (1.A.4.a)Gaseous (fossil)0.043Norm0.022Norm0.138LognLiquid (fossil)0.055Norm0.023Norm1.065NormSolid (fossil)0.087Norm0.040Norm0.994NormBiomass0.103Norm0.055Norm0.730LognAgriculture/forestry/fishing (1.A.4.c)Gaseous (fossil)0.050Norm0.028Norm0.138LognLiquid (fossil)0.051Norm0.029Norm1.065NormSolid (fossil)0.095Norm0.048Norm0.994NormBiomass0.096Norm0.09Norm0.730LognOther stationary (1.A.5.a)Gaseous (fossil)0.097Norm0.023Norm0.138LognLiquid (fossil)0.084Norm0.021Norm1.065NormSolid (fossil)0.103Norm0.033Norm0.994NormBiomass0.180Norm0.04Norm0.730LognAgricultural waste burning (3.F)–1.609Logn0.2Norm0.429NormUncontrolled waste burning (5.C.2)–1.609Logn0.5Norm0.366Logn
Relative uncertainties (fractions) at cell level resulting from
the spatial distribution. The values listed represent the (1 standard
deviation) uncertainty of the emission per cell due to uncertainty sources 2
and 3 as listed in Sect. 2.2.3. All values in the table below are based on
expert quantification and inevitably include a considerable amount of
subjectivity. The data should therefore be considered as a first-order
indication only. Note that the natural logarithm (Ln) of the uncertainty
fraction is given in the event that uncertainty has a lognormal distribution.
Sector nameProxy nameDistributionUncertaintyPublic electricity and heat production; chemical industry; food processing, beverages andtobacco (comb); food and beverages industry; other non-metallic mineral production; small combustion – commercial/institutional – mobileCORINE_2012_Industrial_areaLogn2.2Solid fuel transformation; iron and steel industry (comb); iron and steel production; pulp and paper industry (comb); pulp and paper industry; non-metallic minerals (comb);cement productionCORINE_2012_Industrial_areaLogn3.7Other manufacturing industry (comb); other industrial processes; manufacturing industry – off-road vehicles and other machineryCORINE_2012_Industrial_areaLogn1.4Oil and gas refining (comb); oil and gas refiningCORINE_2012_Industrial_areaLogn3.7TNO_PS for refineriesLogn1.7Coal mining (comb)CORINE_2012_Industrial_areaLogn4.6TNO_PS for coal miningLogn1.7Oil production (comb)CORINE_2012_Industrial_areaLogn1.7TNO_PS for oil productionLogn1.7Gas exploration (comb)CORINE_2012_Industrial_areaLogn1.7TNO_PS for gas productionLogn1.7Coke ovens (comb)CORINE_2012_Industrial_areaLogn1.7TNO_PS for iron and steel – coke ovensLogn1.7Non-ferrous metals (comb); other non-ferrous metal productionCORINE_2012_Industrial_areaLogn3.7TNO_PS for non-ferrous metals – otherLogn1.7Aluminium productionCORINE_2012_Industrial_areaLogn3.7TNO_PS for non-ferrous metals – aluminiumLogn1.7Chemical industry (comb)CORINE_2012_Industrial_areaLogn2.2TNO_PS for chemical industryLogn1.7Passenger carsRoadTransport_PassengerCarsNorm0.3Light duty vehiclesRoadTransport_LightCommercialVehiclesNorm0.3Trucks (> 3.5 t)RoadTransport_HeavyDutyTrucksNorm0.3BusesRoadTransport_BusesNorm0.3MotorcyclesRoadTransport_MotorcyclesNorm0.3MopedsRoadTransport_MopedsNorm0.5Civil aviation – LTOAirport distribution for the year 2015Logn1.4Mobile sources in agriculture/forestry/fishingCORINE_2012_Arable_landLogn1.4Other transportation, including pipeline compressorsPopulation_total_2015Logn3.7Small combustion – residential – household and gardening; other mobile combustionPopulation_total_2015Logn1.3Commercial/institutionalPopulation_total_2015Norm0.5Population_rural_2015Logn1.3Population_urban_2015Logn1.3Wood_use_2014Logn2.2ResidentialPopulation_total_2015Norm0.5Population_rural_2015Logn1.3Population_urban_2015Logn1.3Wood_use_2014Logn1.4Agriculture/forestry/fishingCORINE_2012_Arable_landLogn1.4Wood_use_2014Logn2.2Other stationary combustionPopulation_total_2015Logn1.3Population_rural_2015Logn1.3Wood_use_2014Logn1.4Field burning of agricultural residuesCORINE_2012_Arable_landLogn2.2Population_total_2015Logn2.2Open burning of wasteCORINE_2012_Industrial_areaLogn3.7Population_rural_2015Logn3.7
Spread in the standard deviations if the Monte Carlo simulation
were to be repeated multiple times for a specific sample size, based on a
bootstrapping method.
Data availability
The family of 10 emission inventories is available for non-commercial
applications and research (10.5281/zenodo.3584549, Super et al., 2019).
Author contributions
AJHV assembled the uncertainty data used in this work.
SNCD and HACDvdG are responsible for the base emission
inventory. IS designed the experiments, carried them out and prepared
the manuscript with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was supported by the CO2 Human Emissions (CHE) project,
funded by the European Union's Horizon 2020 research and innovation
programme under grant agreement no. 776186 and the VERIFY project, funded by
the European Union's Horizon 2020 research and innovation programme under
grant agreement no. 776810.
Financial support
This research has been supported by the European Commission
(project CHE (grant no. 776186) and project VERIFY (grant no. 776810)).
Review statement
This paper was edited by Ronald Cohen and reviewed by two anonymous referees.
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