Introduction
The major greenhouse gases (GHGs) – carbon dioxide, methane, and nitrous
oxide – have natural and anthropogenic sources. The synthetic fluorinated
species – chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs),
hydrofluorocarbons (HFCs), and perfluorocarbons (PFCs), sulfur hexafluoride
(SF6) and nitrogen trifluoride (NF3) – are almost or
entirely anthropogenic and are released from industrial and domestic
appliances and applications. Of the synthetic species, tetrafluoromethane
(CF4) and NF3 are emitted nearly exclusively from point
sources of specialised industries (Arnold et al., 2013; Mühle et al.,
2010, Worton et al., 2007). Although these species currently make up only a
small percentage of current emissions contributing to global radiative
forcing, they have potential to form large portions of specific company,
sector, state, province, or even country level GHG budgets.
CF4 is the longest-lived GHG known, with an estimated lifetime of
50 000 years, leading to a global warming potential on a 100-year timescale
(GWP100) of 6630 (Myhre et al., 2013). Significant increases in
atmospheric concentrations are ascribed mainly to emissions from primary
aluminum production during so-called “anode events” when the alumina feed
to the reduction cell is restricted (International Aluminium Institute,
2016), and from the microchip-manufacturing component of the semiconductor
industry (Illuzzi and Thewissen, 2010). Recently, evidence emerged that,
similar to primary aluminium production, rare earth element production may
also release substantial amounts of CF4 (Vogel et al., 2017; Zhang
et al., 2017). Other emission sources for CF4 include release
during the production of SF6 and HCFC-22, but emissions from these
sources are estimated to be small compared to the emissions from the
aluminium production and semiconductor manufacturing industries (EC-JRC/PBL,
2013; Mühle et al., 2010). There is also a very small natural emission
source of CF4, sufficient to maintain the pre-industrial
atmospheric burden (Deeds et al., 2008; Worton et al., 2007).
According to the Intergovernmental Panel on Climate Change (IPCC) fifth
assessment, NF3's global warming potential on a 100-year timescale
(GWP100) is ∼16100 (based on an atmospheric lifetime of
500 years) (Myhre et al., 2013); however, recent work suggests the
GWP100 is higher at 19 700 due to an increased estimate in the
radiative efficiency (Totterdill et al., 2016). Use of NF3 began in
the 1960s in specialty applications, e.g. as a rocket fuel oxidiser and as a
fluorine donor for chemical lasers (Bronfin and Hazlett, 1966). Beginning in
the late 1990s, NF3 has been used by the semiconductor industry,
and in the production of photovoltaic cells and flat-panel displays.
NF3 can be broken down into reactive fluorine (F) radicals and
ions, which are used to remove the remaining silicon-containing deposits in
process chambers (Henderson and Woytek, 1994; Johnson et al., 2000).
NF3 was also chosen because of its promise as an environmentally
friendly alternative, with conversion efficiencies to create reactive F far
higher than other compounds such as C2F6 (Johnson et al., 2000;
International SEMATECH Manufacturing Initiative, 2005). Given its rapid
recent rise in the global atmosphere and projected future market, it has been
estimated that NF3 could become the fastest growing contributor to
radiative forcing of all the synthetic GHGs by 2050 (Rigby et al., 2014).
CF4 and NF3 are not the only species with major point source
emissions. Trifluoromethane (CHF3; HFC-23) is principally made as a
byproduct in the production of chlorodifluoromethane (CHClF2, HCFC-22).
Of the HFCs, HFC-23 has the highest 100-year global
warming potential (GWP100) at 12 400, most significantly due to a long
atmospheric lifetime of 222 years
(Myhre et al., 2013). Its regional
and global emissions have been the subject of numerous previous studies
(Fang et al., 2014, 2015; McCulloch and Lindley, 2007; Miller et al., 2010;
Montzka et al., 2010; Stohl et al., 2010; Li et al.,
2011; Kim et al., 2010; Yao et al., 2012; Keller et al., 2012; Yokouchi et
al., 2006; Simmonds et al., 2018). Thus, emissions of HFC-23 are already
relatively well characterised from a bottom-up and a top-down perspective. In
this work, we will also calculate HFC-23 emissions, not to add to current
knowledge, but to provide a level of confidence for our methodology.
Unlike for HFC-23, the spatial distribution of emissions responsible for
CF4 and NF3 abundances is very poorly understood, which is
hindering action for targeting mitigation. HFC-23 is emitted from well-known
sources (namely HCFC-22 production sites) with well-characterised estimates
of emission magnitudes, and hence it has been a target for successful
mitigation (by thermal destruction) via the clean development mechanism
(Miller et al., 2010). However,
emissions of CF4 and NF3 are very difficult to estimate from
industry level information: emissions from Al production are highly variable
depending on the conditions of manufacturing, and emissions from the
electronics industry depend on what is being manufactured, the company's
recipes for production (such information is not publicly available), and
whether abatement methods are used and how efficient these are under real
conditions. Both the Al production and semiconductor industries have
launched voluntary efforts to control their emissions of these substances,
reporting success in meeting their goals (International Aluminium
Institute, 2016; Illuzzi and Thewissen, 2010; World Semiconductor Council,
2017). Despite the industry's efforts to reduce emissions, top-down studies
on the emissions of CF4 and NF3 have shown the bottom-up
inventories are likely to be highly inaccurate. Most recently, Kim et al. (2014) showed that global bottom-up
estimates for CF4 are as much as 50 % lower than top-down estimates,
and Arnold et al. (2013) showed that the best estimates of
global NF3 emissions calculated from industry information and
statistical data total only ∼35 % of those estimated from
atmospheric measurements.
Accurate emission estimates of NF3 and CF4 are difficult to make
based on simple parameters such as integrated country level uptake rates and
leakage rates, which, for example, underpin calculations of HFC emissions.
Active or passive activities to reduce emissions vary between countries, and
between industries and companies within countries, and the impetus to
accurately understand emissions is lacking in regions that have not been
required to report emissions under the United Nations Framework Convention
on Climate Change (UNFCCC). This problem is compounded by
the difficulty in making measurements of these gases: CF4 and NF3
are the two most volatile GHGs after methane, and have very low atmospheric
abundances, which makes routine measurements in the field at the required
precision particularly difficult. The Advanced Global Atmospheric Gases
Experiment (AGAGE) has been monitoring the global atmospheric trace gas
budget for decades (Prinn et al., 2018). Most recently, AGAGE's “Medusa”
preconcentration GC-MS (gas chromatography–mass spectrometry) system has
been able to measure a full suite of the long-lived halogenated GHGs
(Arnold et al., 2012; Miller et al., 2008). The Medusa is the only
instrument demonstrated to measure NF3 in ambient air samples and the
only field-deployable instrument capable of measuring CF4. The Medusa
on Jeju Island, South Korea, is one of only 20 such instruments currently
in operation globally and is uniquely sensitive to the dominant emission
sources of these compounds given its location in this highly industrial part
of the globe with large capacities of Al production, semiconductor
manufacturing, and rare Earth element production industries. Its utility has
already been demonstrated in numerous previous studies to understand
emissions of many GHGs from Japan, South Korea, North Korea, eastern China,
and surrounding countries (Fang et al., 2015; Kim et al., 2010; Li et
al., 2011).
For the first time, we use the measurements of CF4 (starting in 2008)
and NF3 (starting in 2013) in an inversion framework – coupling each
measurement with an air history map computed using a particle dispersion
model. We demonstrate the use of these measurements to find emission
hotspots in this unique region with minimal use of prior information, and we
show that East Asia is a major source of these species. Focussed mitigation
efforts, based on these results, could have a significant impact on reducing
GHG emissions from specific areas. The technology for abating emissions of
these gases from such discrete sources exists and could be used (Chang
and Chang, 2006; Purohit and Höglund-Isaksson, 2017; Illuzzi and
Thewissen, 2010; Yang et al., 2009; Raoux, 2007; Wangxing et al., 2016).
Methods
Atmospheric measurements
The Gosan station (from here on termed GSN) is located on the south-western
tip of Jeju Island in South Korea (33.29244∘ N, 126.16181∘ E). The station rests at the top of a 72 m cliff, about
100 km south of the Korean Peninsula, 500 km north-east of Shanghai, China,
and 250 km west of Kyushu, Japan, with an air inlet 17 m above ground level (a.g.l.).
A Medusa GC-MS system was installed at GSN in 2007 and has been operated as
part of the AGAGE network to take automated, high-precision measurements for
a wide range of CFCs, HCFCs, HFCs, PFCs, Halons, and other halocarbons, and all
significant synthetic GHGs and/or stratospheric ozone-depleting gases as well
as many naturally occurring halogenated compounds (Miller et al., 2008;
Arnold et al., 2012; Kim et al., 2010). Since November 2013, NF3 has
been measured within this suite of gases. Air reaches GSN from the most
heavily developed areas of East Asia, making the measurements and their
interpretation a unique source for top-down emission estimates in the
region. Ambient air measurements are made every 130 min and are
bracketed with a standard before and after the air sample in order to
correct for instrumental drift in calibration. Further details on the
methodology for the calibration of these gases are given elsewhere
(Arnold et al., 2012; Mühle et al., 2010; Miller et al., 2010; Prinn
et al., 2018).
Atmospheric model
Lagrangian particle dispersion models are well suited to determine emissions
of trace gases on this spatial scale as they can be run backwards, allowing
for the source–receptor relationship to be efficiently calculated. We use
the Numerical Atmospheric dispersion Modelling Environment (NAME III),
henceforth called NAME, developed by the UK Met Office (Ryall and Maryon,
1998; Jones et al., 2007). Inert particles are advected backwards in time by
the transport model, NAME, which also associates a mass to each trajectory.
Hence, NAME output is provided as the time-integrated near-surface (0–40 m) air concentration (g s m-3) in each grid cell – the surface
influence resulting from a conceptual release at a specific rate (g s-1) from the site. “Offline”, this surface influence is divided by the
total mass emitted during the 1 h release time and multiplied by the
geographical area of each grid box to form a new array with each component
representative of how 1 g m-2 s-1 of continuous emissions from a
grid square would result in a measured concentration at the model's release
point (the measurement site). Multiplication of each grid component by an
emission rate then results in a contribution to the concentration.
The meteorological parameter inputs to NAME are from the Met Office's
operational global NWP model, the Unified Model (UM) (Cullen, 1993). The UM
had a horizontal resolution of 0.5625∘×0.375∘ (∼40 km) from December 2007 to April 2010; 0.3516∘×0.2344∘ (∼25 km) from April 2010 to July 2014; and 0.234375×0.15625∘ (∼17 km) from mid-July 2014 to mid-July 2017.
The number of vertical levels in the UM has increased over this period, with
NAME taking the lowest 31 levels in 2009 and the lowest 59 levels in 2015.
The GHGs considered in this study have lifetimes on the order of hundreds to
tens of thousands of years (Myhre et al., 2013) and can be considered inert
gases on the spatial and temporal scales of this study, and therefore the
NAME model schemes for representing chemistry, dry deposition, wet
deposition, and radioactive decay were not used. The planetary boundary layer
height (BLH) estimates are taken from the UM; however, a minimum BLH allowed
within NAME was set to 40 m to be consistent with the maximum emission
height and the height of the output grid. The NAME model was run to estimate
the 30-day history of the air on the route to GSN. We calculated the
time-integrated air concentration (dosage) at each grid box (0.352∘×0.234∘, 0–40 m a.g.l., irrespective of the underlying UM
meteorology resolution) from a release of 1 g s-1 at GSN at 17±10 m a.g.l.
The model is three-dimensional, and therefore it is not just
surface-to-surface transport that is modelled: an air parcel can travel from
the surface to a high altitude and then back to the surface, but only those
times when the air parcel is within the lowest 40 m above the ground will be
included in the model output aggregated sensitivity maps. The computational
domain covers 54.34∘ E to 168.028∘ W longitude (391 grid
cells of dimension 0.352∘) and 5.3∘ S to 74.26∘ N
latitude (340 grid cells of dimension 0.234∘), and extends to more
than 19 km vertically. Despite the increase in the resolution of the UM over
the time period covered, the resolution of the NAME output was kept constant
throughout. For each 1 h period, 5000 inert model particles were used to
describe the dispersion of air. By dividing the dosage (g s m-3) by
the total mass emitted
(3600 s h-1 × 1 h × 1 g s-1) and multiplying
by the geographical area of each grid box (m2), the model output was
converted into a dilution matrix H (s m-1). In Fig. 1, we
show an aggregated dilution matrix for the 2013 inversion period,
demonstrating the areas of most significant influence on the GSN
measurements. Each element of the matrix H dilutes a continuous
emission of 1 g m-2 s-1 from a given grid box over the previous
30 days to simulate an average concentration (g m-3) at the receptor
(measurement point) during a 1 h period.
An aggregation of the dilution matrices from 2013, generated using
NAME output (see Sect. 2.2), illustrating the relative sensitivity of
measurements at GSN to emissions in the region.
Inversion framework
For most long-lived trace gases (with lifetimes of years or longer), the
assumption that atmospheric mole fractions respond linearly to changes in
emissions holds well. By using this linearity, we can relate a vector of
observations (y) to a state vector (x) made up of emissions and other
non-prescribed model conditions (see Sect. 2.6) via a sensitivity matrix
(H) (Tarantola, 2005):
y=Hx+residual.
A Bayesian framework is typically used in trace gas inversions and
incorporates a priori information, which gives rise to the following cost
function:
C=Hx-yTR-1Hx-y+(x-xp)TB-1(x-xp),
where C is the cost function score (the aim is to minimise this score);
H is made up mainly of the model-derived dilution matrices
(Sect. 2.2) but also the sensitivity of changes in domain border conditions
on measured mixing ratios; x is a vector of emissions and domain
border conditions; y is a vector of observations; R is a
matrix of combined model and observation uncertainties; xp
is a vector of prior estimates of emissions and domain border conditions; and
B is an error matrix associated with xp. The
cost function is minimised using a non-negative least squares fit (NNLS)
(Lawson and Hanson, 1974), as previously used for volcanic ash (Thomson et
al., 2017; Webster et al., 2017). The NNLS algorithm finds the least squares
fit under the constraint that the emissions are non-negative. This is an
“active set” method which efficiently iterates over choices for the set of
emissions for which the non-negative constraint is active, i.e. the set of
emissions which are set to zero.
The first term in Eq. (1) describes the mismatch (fit) between the
modelled time series and the observed time series at each observation
station. The observed concentrations (y) are comprised of two distinct
components: (a) the Northern Hemisphere (NH) background concentration,
referred to as the baseline, that changes only slowly over time, and (b) rapidly varying perturbations above the baseline. These observed deviations
above background (baseline) are assumed to be caused by emissions on a
regional scale that have yet to be fully mixed on the hemisphere scale. The
magnitude of these deviations from baseline and, crucially, how they change
as the air arriving at the stations travels over different areas, is the key
to understanding where the emissions have occurred. The inversion system
considers all of these changes in the magnitude of the deviations from
baseline as it searches for the best match between the observations and the
modelled time series. The second term describes the mismatch (fit) between
the estimated emissions and domain border conditions (x) and prior
estimated emissions and domain border conditions (xp) considering the
associated uncertainties (B).
The aim of the inversion method is to estimate the spatial distribution of
emissions across a defined geographical area. The emissions are assumed to
be constant in time over the inversion time period (in this case, one
calendar year, as is typically reported in inventories). Assuming the
emissions are invariant over long periods of time is a simplification but
is necessary given the limited number of observations available. In order to
compare the measurements and the model time series, the latter are converted
from air concentration (g m-3) to the measured mole fraction, e.g.
parts per trillion (ppt), using the modelled temperature and pressure at the
observation point.
Prior emission information
Global emission estimates of CF4 and NF3 using atmospheric
measurements have demonstrated that bottom-up accounting methods for one
or more sectors, or one or more regions, are highly inaccurate (Arnold et
al., 2013; Mühle et al., 2010). This study makes no effort to improve
such inventory methods but instead focusses on minimising the reliance of
prior information on our Bayesian-based posterior emission estimates. Our
prior information data sets come from the Emissions Database for Global
Atmospheric Research (EDGAR) v4.2 emission grid maps (EC-JRC/PBL,
2013). This data set only covers the years 2000 to 2010, and therefore we
apply the prior for 2010 for each year between 2011 and 2015. The 0.1×0.1∘ EDGAR emission maps were first regridded based on the
lower resolution of our inversion grid (0.3516∘×0.2344∘). In order to remove the influence of the within-country
prior spatial emission distribution, each country's emissions were then
averaged across their entire landmass (see Fig. S1 in the Supplement). We applied
five different levels of uncertainty to each inversion grid cell (a,b) in
five separate inversion experiments, each a multiple of the emission magnitude
(xa,b) in each grid cell: 1×xa,b (i.e. 100 %
uncertainty), 10×xa,b, 100×xa,b,
1000×xa,b, and 10000×xa,b. We were then able
to test the sensitivity of the prior emission uncertainty and provide
evidence for the low influence of prior information on the emission
estimates in the posterior.
Measurement–model and prior uncertainties
In addition to inaccurate prior information, another significant source of
uncertainty in estimating emissions is from the model, from both the input
meteorology and the atmospheric transport model itself. The uncertainty
matrix, R, is a critical part of Eq. (1) that allows us to adjust
uncertainties assigned to each measurement depending on how well we think
the model is performing at that time. It describes, per hour time period, a
combined uncertainty of the model and the observation at each time. The
method of assigning measurement–model uncertainties is under development and
here we describe one method that has been applied to the modelling of GSN
measurements. All elements of the modelled meteorology (wind speed and
direction, BLH, temperature, pressure, etc.) are important in understanding
the dilution and uncertainty in modelling from source to receptor. However,
quantifying the impact of each element that each model particle experiences
in order to fully quantify the model uncertainty at each measurement time is
beyond what is available from numerical weather prediction models. So in
order to attempt to quantify a model/observation uncertainty we took a
pragmatic approach and used modelled BLH at the receptor as a proxy.
Emissions are primarily diluted by transport and mixing within the planetary
boundary layer (PBL), and hence modelling of the PBL height (BLH) is crucial
for accurate modelling of the mixing ratios. Changes in BLH at or surrounding
the measurement location can cause significant changes to the measured mixing
ratio. A low BLH (causing a larger model uncertainty) has two implications
for measurements at the Gosan site. The first implication is a greater
possibility of air from above the PBL being sampled in reality but not in the
model. Subtle changes in the BLH at the exact measurement location are not
well modelled and the difference between sampling above or within the PBL can
have a significant influence on the amount of pollutant assigned to a back
trajectory. The second implication is greater influence of emissions from
sources very near GSN. A lower BLH means that a lower rate of dilution of
local emissions will occur, in turn increasing the signal of the local
pollutant above the baseline. A relatively small change in a low BLH will
have a significant influence on this dilution compared to the same change on
a high BLH. Thus, any error in the BLH at low levels can significantly
amplify the uncertainty in the pollutant dilution. This is coupled with the
fact that the modelled BLH has significant uncertainty especially when low.
To assign a model uncertainty to each hourly window of measurements, we use
model information of BLH:
σmodel=σbaseline×fBLH,
where σbaseline is the variability associated with the baseline
calculation (see Sect. 2.6), and fBLH is a multiplying factor
(greater than or less than unity) that increases or decreases the relative
uncertainty assigned to each model time period. fBLH is based on
modelled BLH magnitude and variability over a 3 h period and is
calculated with the following:
fBLH=MaxBLH-inletMinBLH-inlet×ThresholdMinBLH,
where MaxBLH-inlet is the largest of either 100 m or the maximum
distance, calculated hourly, between the inlet and the modelled BLH within a
period of 3 h around the measurement time; MinBLH-inlet is the
smallest of the distances calculated between the inlet and the BLH over the
same 3 h period; “Threshold” is an arbitrary value set at 500 m; and
MinBLH is the lowest BLH recorded over the 3 h period. Thus,
the relative assigned uncertainty considers the proximity of the varying BLH
to the inlet height and a recognition that observations taken when the BLH is
varying at higher altitudes (> 500 m a.g.l.) is likely to have
less impact and therefore have lower uncertainty compared to those taken when
the BLH is varying at lower altitudes (< 500 m a.g.l.).
Figures S2–S6 show annual time series of observations and the
corresponding measurement–model uncertainties, as well as statistics for the
mismatch between observations and modelled time series.
Baseline calculation and domain border conditions
For each measurement at GSN, it is important to accurately understand the
portion of the total mixing ratio arriving from outside the inversion domain
and the portion from emission sources within the domain; otherwise, emissions
from specific areas could be over- or underestimated. GSN is uniquely
situated, receiving air masses from all directions over the course of the
year, which can have distinct compositions of trace gases, driven mainly by
the different emission rates between the two hemispheres and slow
interhemispheric mixing.
In addition to the time-integrated air concentration produced by NAME
(Sect. 2.2), the 3-D coordinate where each particle left the computational
domain was also recorded. This information was then post-processed to
produce the percentage contributions from 11 different borders of the 3-D
domain (Fig. 2). From 0 to 6 km in height, eight horizontal boundaries
(WSW, WNW, NNW, NNE, ENE, ESE, SSE, and SSW) were considered, and between 6 and 9 km the horizontal boundaries were only split between north and south. The
11th border was considered when particles left in any direction above 9 km. Thus, the influence of air arriving at GSN from outside the domain was
simplified as a combination of air masses arriving from 11 discrete
directions.
Schematic of the domain borders as applied in the inversion. A total of 11
domain border conditions were estimated as depicted from 1 to 11 as a
multiplying factor to the prior baseline estimated using data from the Mace
Head observatory. Below 6 km, the domain border was divided eight times: NNE,
ENE, ESE, SSE, SSW, WSW, WNW, and NNW; between 6 and 9 km, the domain border
was just divided between north and south; and air arriving from above 9 km
was considered from one “high” domain border. Average posterior multiplying
factors for CF4 over the 8 years were 1.00±0.01 (NNE), 0.97±0.06 (ENE), 1.02±0.05 (ESE), 0.99±0.01 (SSE), 1.00±0.01 (SSW), 0.99±0.01 (WSW), 1.00±0.00 (WNW), 1.00±0.01 (NNW), 1.00±0.00 (6 to 9 km north), 1.00±0.05
(6 to 9 km south), and 0.97±0.03 (above 9 km).
We use measurements from the Mace Head observatory (from here termed MHD) on
the west coast of Ireland (53.33∘ N, 9.90∘ W) – a key
AGAGE site providing long-term in situ atmospheric measurements
– to act as a starting point for an estimate of the composition of air from
the NH midlatitudes entering the East Asian domain. MHD was one of the first
locations to measure CF4 (starting 2004) and NF3
(starting 2012), and other measurements from the site are routinely used in
atmospheric studies to calculate decadal trends in the NH atmospheric
abundances. In summary, a quadratic fit was made only to MHD observations
that were representative of the NH baseline, i.e. when well-mixed air was
arriving predominately from the WNW–NNW (North Atlantic) direction as
calculated using NAME (details of filtering and fitting are given in the
Supplement).
The composition of air arriving from any of the 11 directions is calculated
using corresponding multiplying factors applied to the MHD baseline, which
were included as part of the state vector (x); i.e. these factors are
constant for a given inversion year. The prior baseline was therefore
perturbed as part of the inversion based on the relative contribution of air
arriving from different borders of the 3-D domain and the multiplying factors
that are included within the cost function (Eq. 1). Figure 3 shows an
annual time series of observations for CF4 and the difference between
the prior baseline (the quadratic fit from MHD) and the posterior baseline.
Time series of CF4 measurements during 2013 – an example
year with the most uninterrupted time series. Prior baseline (blue) is
adjusted in the inversion using the baseline condition variables, producing
a posterior baseline (red). During the summer months, the proportion of air
arriving from the south significantly rises, causing a large shift in the
posterior baseline relative to the prior baseline calculated from Mace Head
data.
Domains and inversion grids
The domain used in the inversion is smaller than the computational NAME
transport model domain. The horizontal inversion domain covers
88.132 to 145.860∘ E longitude (164 fine grid cells
of 0.352∘) and 15.994 to 57.646∘ N
latitude (178 fine grid cells of 0.234∘). GSN is within a region
surrounded by countries with major developed industries, and therefore the
site is relatively insensitive to emissions from further away that are
diluted on the route to the site. NAME is run on a larger domain to ensure that
on the occasion when air circulates out of the inversion domain and then
back, its full 30-day history in the inversion domain is included.
An initial computational inversion grid (from here termed the “coarse grid”)
was created based on (a) aggregated information from the NAME footprints over
the period of the inversion (in this case, 1 year), aggregating fewer grid
cells in areas that are “seen” the most by GSN, and (b) the prior
emissions flux; i.e. areas known to have low emissions (e.g. ocean) had
higher aggregation. Coarse grid cells could not be aggregated over more than
a single country/region and a total of ≈100 coarse grid cells (n)
were created. After the initial inversion, a coarse grid cell was chosen to
divide in two by area. The decision on which single coarse grid cell to
split is calculated based on the posterior emission density (g yr-1 m-2) of the coarse grids and the ability of the posterior emissions to
impact the measurements at GSN (using information from the NAME output). A
new inversion was run using identical inputs except for the number of grid
cells (now n+1). This sequence was repeated 50 times, creating ≈150 coarse grid cells within the inversion domain for the final inversion.
The results from the inversions with the maximum disaggregation are
presented in this paper.
Annual posterior emission estimates for the five main emitting
countries surrounding GSN (Gg yr-1). These posterior emission
estimates are from the inversion that uses a prior emission uncertainty on
each fine grid cell of 100 times the prior emission rate.
CF4
NF3
HFC-23
China
S. Korea
N. Korea
Japan
Taiwan
China
S. Korea
N. Korea
Japan
Taiwan
China
S. Korea
N. Korea
Japan
Taiwan
2008
4.66
0.31
0.05
0.57
0.01
6.8
0.09
0.08
0.28
0.11
(1.82)*
(0.05)*
(0.12)*
(0.36)*
(0.07)
(4.3)
(0.09)
(0.28)
(0.69)
(0.15)
2009
4.01
0.15
0.02
0.23
0.32
5.2
0.04
0.00
0.29
0.00
(1.80)
(0.05)
(0.10)
(0.33)
(0.17)
(5.1)
(0.12)
(0.29)
(0.84)
(0.48)
2010
4.42
0.29
0.00
0.10
0.06
9.2
0.04
0.00
0.02
0.00
(2.06)
(0.05)
(0.16)
(0.48)
(0.13)
(6.4)
(0.10)
(0.39)
(1.11)
(0.31)
2011
4.12
0.32
0.06
0.18
0.00
8.4
0.09
0.00
0.26
0.00
(2.37)
(0.05)
(0.15)
(0.67)
(0.26)
(5.1)
(0.08)
(0.27)
(0.69)
(0.41)
2012
8.25
0.29
0.00
0.16
0.04
10.7
0.10
0.00
0.06
0.24
(2.59)
(0.05)
(0.13)
(0.60)
(0.40)
(4.6)
(0.07)
(0.23)
(0.67)
(0.46)
2013
2.82
0.26
0.08
0.11
0.09
(2.49)
(0.04)
(0.13)
(0.48)
(0.26)
2014
5.35
0.21
0.07
0.21
0.00
1.08
0.40
0.02
0.75
0.03
(2.61)
(0.05)
(0.15)
(0.50)
(0.30)
(1.17)
(0.05)
(0.12)
(0.36)
(0.09)
2015
4.33
0.36
0.00
0.36
0.00
0.36
0.60
0.15
0.11
0.00
(2.65)
(0.11)
(0.26)
(0.57)
(0.44)
(1.36)
(0.07)
(0.16)
(0.39)
(0.27)
* Kim et al. (2010) estimated CF4 emissions from China in
the range 1.7–3.1 Gg yr-1 and Li et al. (2011) in the range 1.4–2.9 Gg yr-1. For South and North Korea (combined), Li et al. (2011)
estimated emissions of CF4 at 0.19–0.26 Gg yr-1 and from Japan at
0.2–0.3 Gg yr-1.
Results and discussion
Country total emission estimates
Table 1 provides a summary of our estimates of emissions from the five major
emitting countries/regions within the East Asian domain. These posterior
emission estimates use a prior emission uncertainty in each fine grid cell
of 100 times the emission magnitude (see Sect. 2.4).
HFC-23
Time series of country emission totals (2008–2015). Annual
inversion results are given for each gas for three different levels of
uncertainty applied to the prior emission map: 100, 1000, and 10 000 times
the emission magnitude for each grid cell. The aggregated country totals from
the prior data set are also given. Posterior uncertainties are shown for the 100 times prior uncertainty scenario.
Fang et al. (2015) conducted a very thorough bottom-up
study within their work on HFC-23, constraining an inversion model using
both prior information and atmospheric measurements. They used an inverse
method based on the FLEXible PARTicle dispersion model (FLEXPART) using measurements from three sites in East Asia –
GSN, Hateruma (a Japanese island ∼200 km east of Taiwan), and
Cape Ochiishi (northern Japan), calculating an HFC-23 emission rise in
China from 6.4±0.7 Gg yr-1 in 2007 (6.2±0.6 Gg yr-1 in 2008) to 8.8±0.8 Gg yr-1 in 2012. An earlier study
by Stohl et al. (2010) also reports HFC-23 emissions
of 6.2±0.8 Gg yr-1 in 2008. Both Fang et al. (2015) and Stohl et al. (2010)
report emissions from other countries below 0.25 Gg yr-1 for all years.
Our estimates use a completely independent inverse method and only data from
GSN, yet the results are very close to those of Fang et al. (2015) (Fig. 4) – 6.8±4.3 Gg yr-1 in 2008 (a
difference of 10 %) and 10.7±4.6 Gg yr-1 in 2012 (a
difference of 22 %) – and of Stohl et al. (2010).
The posterior uncertainties in these two different studies mainly reflect
the difference in the prior uncertainty assumed for the prior information.
We assume a very high level of uncertainty on our prior emissions, and
therefore our posterior uncertainties are significantly higher. However,
these inversion result estimates are lower than estimates based on
interspecies correlation analysis by Li et al. (2011) who
calculated emissions of HFC-23 from China in 2008 in the range of 7.2–13 Gg yr-1. Using a CO tracer-ratio method,
Yao et al. (2012) estimated particularly low emissions of
2.1±4.6 Gg yr-1 for 2011–2012. The estimates derived from
atmospheric inversions do not rely on any correlations with other species or
known emissions for certain species and, given two separate inversion
studies, have produced very similar results. We suggest these provide a more
reliable top-down emission estimate of HFC-23. As well as providing an
independent validation of the previous work on HFC-23 by Fang et al. (2015) and Stohl et al. (2010), the
alignment of our HFC-23 emission estimates with those previous studies
provides confidence in our inversion methodology for the CF4 and
NF3 emission estimates.
The effect of the regridding routine on posterior emission
distributions for CF4. Panels (a), (c), and
(e) are posterior emission maps at the initial inversion resolution,
at 0 regridding steps, at 25 regridding steps, and at 50 regridding steps,
respectively. Panels (b), (d), and (f) show the
emission magnitude minus the uncertainty calculated for each inversion grid
box at the same regridding levels (0, 25, and 50), which demonstrates the
relative uncertainty of the emission distribution obtained for South Korea.
Results are from inversions with initial uncertainty on the prior emission
field set to 100 times the emissions at each fine grid square. Units are in
g m-2 yr-1.
Emission maps for all years of data available for CF4.
Results are from inversions with initial uncertainty on the prior emission
field set to 100 times the emissions at each fine grid square. Units are in
g m-2 yr-1; see Fig. S7 for corresponding maps of emission
magnitude minus the uncertainty.
CF4
Our understanding of emissions of CF4 and NF3 is very
poor, which is highlighted in global studies based on atmospheric
measurements that show bottom-up estimates of emissions are significantly
underestimated (Mühle et al., 2010; Arnold et al., 2013). With such a
poor prior understanding of emissions, we assess the effect of prior
uncertainty on the posterior emissions (Fig. 4). With assignment of
uncertainty on the prior of each fine grid cell at 10 times the prior
emission value, the posterior is still significantly constrained by the prior
for both China and South Korea. When larger uncertainties are applied to the
prior (100 times to 10 000 times), the posterior estimates are very
consistent, indicating that when greater than 100× uncertainty is
applied, emission estimates are most significantly constrained by the
atmospheric measurements. For China, for 7 of the 8 years studied, our
posterior estimates are greater than twice the prior estimates taken from
EDGAR v4.2. The latest global estimates are from Rigby et al. (2014) and they
estimated global CF4 emissions of 10.4±0.6 Gg yr-1 in
2008 with a steady but small increase to 11.1±0.4 Gg yr-1 in
2013 (with the exception of a dip in 2009 to 9.3±0.5 Gg yr-1). We
highlight that our Chinese emission estimates remain within a narrow range
for 5 of the 8 years studied at between 4.0 and 4.7 Gg yr-1 (with
typical uncertainties < 2.7 Gg yr-1), and for 7 of the
8 years studied between 2.82 and 5.35 Gg yr-1. However, the estimate
for 2012 appears to be anomalous at 8.25±2.59 Gg yr-1. In
relation to the global top-down estimates from 2008 to 2012, our Chinese
estimates represent between 37 and 45 % of global emissions between 2008
and 2011 with a jump to 74 % in 2012. This significant increase in 2012
is not reconcilable with atmospheric measurements on the global scale and is
very likely a spurious result of the inversion. The most probable explanation
for such a result is the incorrect assignment of emissions on the inversion
grid. Incorrect assignment of emissions can occur between countries,
particularly where air parcels frequently pass over more than one country,
therefore reducing the ability of the inversion to confidently place
emissions. However, there is not an obvious drop in emissions for another
country in 2012 that would offset the large increase in the Chinese emission
estimate. Within a country, incorrect assignment of emissions from an area
closer to the receptor to an area further from the receptor will increase the
calculated total emissions due to increased dilution in going from a near to
a far source. Our inversion is susceptible to this effect as we only have one
site for assimilation of measurements; two measurement sites, spaced apart
and straddling the area of interest, would provide significantly more
information to constrain the spatial emission distribution.
Our estimates are significantly higher than emission estimation methods
using interspecies correlation: Kim et al. (2010) estimated
CF4 emissions in the range of only 1.7–3.1 Gg yr-1 in 2008 and Li
et al. (2011) only 1.4–2.9 Gg yr-1 over the same period. The
interspecies correlation approach inherently requires that the sources of
the different gases that are compared are coincident in time and space. Kim
et al. (2010) and Li et al. (2011) used HCFC-22 as the
tracer compound for China with a calculated emission field from an inverse
model, and most emissions of this gas originate from fugitive release from
air conditioners and refrigerators. However, CF4 is emitted mostly from
point sources in the semiconductor and aluminium production industries with
different spatial emission distribution within countries, and likely
different temporal characteristics compared to HCFC-22.
Emission estimates from South Korea and Japan are 1 order of magnitude
lower than those from China. For 2008, Li et al. (2011) estimate emissions
of CF4 from the combination of South and North Korea of 0.19–0.26 Gg yr-1
and from Japan of 0.2–0.3 Gg yr-1, which are on the low end of the
uncertainty range of our estimates for that year (Table 1). As one of the
largest, if not the largest, countries for semiconductor wafer production,
Taiwan is also an emitter of CF4. However, measurements at GSN provide
only poor sensitivity to detection of emissions from Taiwan, and our results
can only suggest that emissions are likely < 0.5 Gg yr-1. North
Korean emissions were small and no annual estimate was above 0.1 Gg yr-1.
Emission maps for both years of data available for NF3:
panels (a) and (c) show posterior emission maps for the
years 2014 and 2015, respectively. Panels (b) and (d) show
the emission magnitude minus the uncertainty calculated for each inversion
grid box for the years 2014 and 2015, respectively. Results are from
inversions with initial uncertainty on the prior emission field is set to 100
times the emissions at each fine grid square. Units are in
g m-2 yr-1.
As for Fig. 6 but for HFC-23.
NF3
Our understanding of NF3 emissions from inventory and industry data is
even poorer than for CF4. On a global scale, the emission estimates from
industry are underestimated (Arnold et al., 2013). This
study suggests that at least some emissions of NF3 stem from China;
however, gaining meaningful quantitative estimates has been difficult due to
large uncertainties (Fig. 4). Contrastingly, the posterior estimates of
emissions from South Korea have relatively small uncertainties. Emissions
from China travel a greater distance to the measurement site compared to
emissions from South Korea. Thus, the magnitudes of NF3 pollution
events from China (especially from provinces furthest west), in terms of the
mixing ratio detected at GSN, are smaller than for pollution arriving from
neighbouring South Korea. Also, the poorer measurement precision for
NF3 compared to CF4 leads to a larger uncertainty on the baseline,
which in turn affects the certainty on the pollution episode, especially for
more dilute signals. Emission estimates for Japan are difficult to make
without improved prior information and more atmospheric measurements in
other locations. We argue that other large changes in our emission
estimates from 2014 to 2015 could be real. For example, Japan's National
Inventory Report for NF3 shows a reduction in emissions of 63 %
between 2013 and 2015 (Ministry of the Environment Japan et al., 2018),
which is within the uncertainty of the relative rate of decrease we observe.
As for CF4, emission estimates of NF3 from Taiwan and
North Korea are highly uncertain. However, our results do indicate that
emissions of NF3 from Taiwan might be lower than from South Korea
despite very similar-sized semiconductor production industries. Focussing on
the more meaningful estimates from South Korea, emissions of NF3 in
2015 are estimated to be 0.60±0.07 Gg yr-1 which equates to
9660±1127 Gg yr-1 CO2 eq. emissions (based on a
GWP100 of 16 100). This is ∼1.6 % of the country's
CO2 emissions (Olivier et al., 2017), thus making a significant
impact on their total GHG budget. Further, given that the sources of
NF3 are relatively few, these emissions can be assigned to a small
number of industries, potentially making NF3 an easy target for
focussed mitigation policy. Rigby et al. (2014) updated the global emission
estimates from Arnold et al. (2013), and calculated an annual emission
estimate of 1.61 Gg yr-1 for 2012, with an average annual growth rate
over the previous 5 years of 0.18 Gg yr-1. Linearly extrapolating this
growth to 2014 and 2015 leads to projected global emissions of 1.97 and
2.15 Gg yr-1 for 2014 and 2015, respectively. Thus, South Korean
emissions as a percentage of these global totals equate to ∼20 %
and ∼28 % for 2014 and 2015, respectively, which is around the
proportion of semiconductor wafer fabrication capacity in South Korea
relative to global totals (∼20 %) (SEMI, 2017).
Spatial emission maps
We use “emissions minus uncertainty” maps (e.g. Fig. 5b) to provide
information on where we are most certain of large emissions, i.e. where
emission hotspots are located and if they are significant: less negative
values indicate more certainty, with positive values indicating that the
uncertainty is less than the best estimate and negative values indicating
that the uncertainty is bigger than the estimate. A more common way to
illustrate grid-level uncertainty is in an “uncertainty reduction” map. This
works well when starting from a relatively well-constrained, spatially
resolved prior to illustrate the additional constraint the atmospheric
observations add. In this study, however, we are starting from very poor
prior information and we generate a posterior emission map that is very
distinct from the prior, informed largely by the measurements. Thus, an
uncertainty reduction map provides little useful information.
Figure 5 shows the effect of regridding over the course of 50 separate
CF4 inversions (for 2015), from zero regridding steps (i.e. using a
coarse grid space determined using information from NAME and the prior
emissions), through to 25, and then 50 steps. The inversion was not allowed
to decrease the minimum posterior grid size beyond four fine grid squares
(i.e. 4 times the 0.3516∘×0.2344∘ grid square). This
method highlights the areas that have the highest emission density; the
splitting of these grid cells improves the correlation between observations
and posterior model output. However, these emission maps must be studied
alongside the corresponding uncertainty maps. The inversion could continue to
split towards a fine grid resolution limit even though there may not be
enough information in the data to accurately constrain emissions from each
course grid cell (leading to spurious emission patterns) and the process
would be computationally very expensive. The largest emissions of
CF4 arise from China, and Fig. 5 suggests the largest emissions
come from an area between 35 and 38∘ N. The uncertainty on these
emissions from the specific final coarse grid squares is large, and therefore
care needs to be taken not to overinterpret emission hotspots. Although the
grid is being split, it is not realistic for the model to correctly interpret
the spatial distribution of emissions at this distance from GSN, and this is
demonstrated in Fig. 5f where the relative error on emissions in this corner
of the domain is large. Without better prior information, it is not possible
to distinguish between real year-to-year emission pattern changes and
inaccurate emission patterns (Figs. 6 and S7). Over the period of study,
emissions of CF4 generally appear to arise from north of
30∘ N, and in 2008 and 2013 emissions appear around 25∘ N.
However, GSN does not have good sensitivity to emissions from this area and
it is possible that these emissions could be incorrectly assigned from
Taiwan. Although emissions from South Korea are significantly lower than for
China, the proximity to GSN causes the grid cells to be split and emissions
to be assigned at higher spatial resolution and generally (except for 2008)
in the north-west quadrant of the country. Splitting of grid cells in South
Korea decreased the relative error on the emissions from particular grid
squares, providing confidence that the placement of emissions is accurate.
Further, for the sequential years 2013, 2014, and 2015, two specific grid
cells in that north-west quadrant of South Korea are
are highlighted with comparatively low uncertainties (Fig. S7). How well
these consistent year-to-year emission patterns in South Korea correlate with
the actual location of emissions needs to be the subject of further study
(e.g. improved bottom-up inventory compilation efforts). Emissions from Japan
are too uncertain to explore the spatial emissions pattern.
For NF3, emissions from China and Japan are too low and uncertain to
interpret at finer spatial resolution. However, as with CF4, it is
interesting to study the relatively more certain spatially disaggregated
emissions from South Korea (Fig. 7). In common with CF4, NF3
emissions from the south-west area are minimal; however, in contrast to
CF4, emissions occur on the eastern side of South Korea and on the
south-east coast. Emissions from the south-east coast coincide with the
known location of a production plant for NF3 located in the area of
Ulsan (Gas World, 2011). If this plant is sufficiently separated in
space from the end-users of NF3, then this result would indicate that
production of NF3, not just use, could be a significant source in South
Korea.
The study of Fang et al. (2015) highlights three major
hotspots for HFC-23 emissions in China based on HCFC-22 production facility
locations. Our posterior maps (Fig. 8) correctly show the bulk of
emissions in far eastern China, in line with the results of Fang et al. (2015). However, given the inconsistency of emission maps
between years, we are unable to provide any more information without a better
spatially disaggregated prior emission map.