High frequency, in situ observations from 11 globally distributed sites
for the period 1994–2014 and archived air measurements dating from 1978
onward have been used to determine the global growth rate of
1,1-difluoroethane (HFC-152a, CH
HFC-152a (CH
HFC-152a has the smallest 100-year global warming potential (GWP
Ryall et al. (2001) using observations from Mace Head, Ireland reported the
distribution of European HFC-152a emissions, concentrated in Germany, and
estimated an average European total emission of 0.48 Gg yr
In the Southern Hemisphere HFC-152a monthly means, annual means and trends
have been reported from observations at Cape Grim, Tasmania, for 1998–2004
(Sturrock et al., 2001; Fraser et al., 2014a; Krummel et al., 2014). The
HFC-152a annual means have grown from 0.8 ppt (0.1 ppt yr
Here we further expand the HFC-152a record up to the end of 2014 using in
situ observations from 11 globally distributed monitoring stations (9
Advanced Global Atmospheric Gases Experiment (AGAGE) stations and 2
affiliated stations), together with atmospheric transport models to
independently estimate HFC-152a emissions on regional and global scales. We
then compare these with HFC-152a emission estimates compiled from national
reports to the United Nations Framework Convention on Climate Change
(UNFCCC) and Emissions Database for Global Atmospheric Research (EC-JRC/PBL EDGAR v4.2;
High frequency, in situ measurements of HFC-152a were made by gas
chromatography-mass spectrometry (GC-Agilent 6890) coupled with quadrupole
mass selective detection (MSD-Agilent 5973/5975). Measurements commenced at
Mace Head, Ireland in 1994 and Cape Grim, Tasmania in 1998, using a
custom-built automated pre-concentration system (adsorption desorption
system – ADS) to selectively and quantitatively retain halogenated compounds
from 2 L air samples. Based on a Peltier–cooled pre-concentration microtrap
cooled to
Overview of the 11 measurement stations used in this study, their coordinates and periods for which data are available.
Table 1 lists the geographical location and the time when routine ambient
measurements of HFC-152a began at each monitoring station. Stations with the
longest observational records that deployed both ADS and Medusa GC-MS
instruments include Mace Head (MHD), Jungfraujoch (JFJ), Ny-Ålesund
(ZEP) and Cape Grim (CGO). Medusa GC-MS instruments were installed at five
other AGAGE stations Trinidad Head (THD), Gosan (GSN), Ragged Point, (RPB),
Shangdianzi (SDZ), and Cape Matatula (SMO) between 2003 and 2010. In addition
two AGAGE affiliated stations Monte Cimone (CMN) and Hateruma (HAT), which
use comparable GC-MS instruments, but a different pre-concentration design
for sample enrichment, commenced HFC-152a measurements in 2001 and 2004,
respectively. Importantly, all 11 stations listed in Table 1 report
HFC-152a measurements relative to the Scripps Institution of Oceanography
(SIO-05) calibration scale (as dry gas mole fractions in pmol mol
The estimated accuracy of the calibration scale for HFC-152a is 4 %: a
more detailed discussion of the measurement technique and calibration
procedure is reported elsewhere (Miller et al., 2008; O'Doherty et al.,
2009; Mühle et al., 2010). HFC-152a was determined using the MS in
selected ion monitoring mode (SIM) with a target ion CH
In order to extend the HFC-152a data record back before the commencement of high-frequency measurements, analyses of Northern Hemisphere (NH) and Southern Hemisphere (SH) archived air samples dating back to 1978, were carried out using three similar Medusa GC-MS instruments at the Scripps Institution of Oceanography (SIO), La Jolla, California, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Aspendale, Australia and the Cape Grim Baseline Air Pollution Station, Tasmania. The SH samples are part of the Cape Grim air archive (CGAA) described in Langenfelds et al. (1996), and Krummel et al. (2007). The NH samples analyzed for this paper were filled during background conditions mostly at Trinidad Head, but also at La Jolla, California; Cape Meares, Oregon; Ny Ålesund, Svalbad and Point Barrow, Alaska (some samples are courtesy of the National Oceanic and Atmospheric Administration (NOAA).
In addition, eight SH samples were measured at SIO and compared with SH
samples of similar age measured at CSIRO (February 1995, July 1995, November 1995, June 1998, July 2004, February 2006, August 2008, and December 2010,
Baseline in situ monthly mean HFC-152a mole fractions were calculated by excluding values enhanced by local and regional pollution influences, as identified by the iterative AGAGE pollution identification algorithm, (see Appendix in O'Doherty et al., 2001). Briefly, baseline measurements are assumed to have a Gaussian distribution around the local baseline value, and an iterative process is used to filter out the points that do not conform to this distribution. A second-order polynomial is fitted to the subset of daily minima in any 121-day period to provide a first estimate of the baseline and seasonal cycle. After subtracting this polynomial from all the observations a standard deviation and median are calculated for the residual values over the 121-day period. Values exceeding 3 standard deviations above the baseline are thus identified as non-baseline (polluted) and removed from further consideration. The process is repeated iteratively to identify and remove additional non-baseline values until the new and previous calculated median values agree within 0.1 %. For the core AGAGE stations, in situ baseline data and archive air data, extending the record to periods prior to the in situ measurement period, are then combined for each hemisphere, and outliers are rejected by an iterative filter.
We pursued several approaches to determine emissions at global, continental and regional scales. The methodologies have been published elsewhere and are summarized below. The global, continental and some regional estimates incorporate a priori estimates of emissions, which were subsequently adjusted using the observations.
Estimates of global emissions of HFC-152a (Gg yr
There are several sources of information on production and emissions of
HFC-152a; none of which, on their own, provides a complete database of
global emissions. The more geographically comprehensive source of
information is provided by the parties to the UNFCCC, but only includes
Annex 1 countries (developed countries). The 2014 database covers years 1990
to 2012 and are reported in Table 2(II) s1 in the common reporting format
(CRF) available at
To infer “top-down” emissions we select observations from the various observing sites listed in Table 1 and four chemical transport models. These 11 sites are sensitive to many areas of the world in which HFC-152a emissions are reported; however, other areas of the globe that are not well monitored by this network are also likely to have significant emissions (such as South Asia, South Africa, and South America).
To estimate global-average mole fractions and derive growth rates, a
two-dimensional model of atmospheric chemistry and transport was employed.
The AGAGE 12-box model simulates trace gas transport in four equal mass
latitudinal sections (divisions at 30–90
We used the methodology outlined in Lunt et al. (2015) and Rigby et al. (2011b) to derive emissions of HFC-152a from continental regions. The high-resolution, regional UK Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME), Manning et al. (2011) was used to simulate atmospheric HFC transport close to a subset of AGAGE monitoring sites, which were strongly influenced by regional HFC sources (domains shown by red boxes in Fig. 1). Simultaneously, the influence of changes to the global emissions field on all measurement stations was simulated using the global Model for OZone and Related Tracers, MOZART (Emmons et al., 2010). We estimated annual emissions for the period 2007–2012 and aggregated the derived emissions fields into continental regions, separating countries that either do (“Annex-1”), or do not (“non-Annex-1”) report detailed, annual emissions to the UNFCCC. Emissions were estimated using a hierarchical Bayesian inverse method (Ganesan et al., 2014; Lunt et al., 2015) and all high-frequency observations from 10 of the 11 sites listed in Table 1, excluding Shangdianzi due to the short time series. The hierarchical Bayesian method includes uncertainty parameters (e.g., model “mismatch” errors and a priori uncertainties) in the estimation scheme, reducing the influence of subjective choices on the outcome of the inversion.
Location of AGAGE and affiliated stations. Ny-Ålesund, Zeppelin, Norway (ZEP); Mace Head, Ireland (MHD);
Jungfraujoch, Switzerland (JFJ); Monte Cimone, Italy (CMN); Trinidad Head,
USA (THD); Shangdianzi, China (SDZ); Gosan, South Korea (GSN); Hateruma,
Japan (HAT); Ragged Point, Barbados (RPB); Cape Matatula, American Samoa
(SMO); and Cape Grim, Tasmania (GCO). Red boxes indicate “local regions”
where the NAME model was used with increased resolution compared to the
global MOZART model, Annex 1 countries are shaded blue and non-Annex 1
countries are shaded yellow.
Note:
A method for estimating emissions from observations and atmospheric transport modeling with NAME referred to as InTEM, “Inversion Technique for Emission Modelling” (Manning et al., 2011), uses a simulated annealing method (Press et al., 1992) to search for the emission distribution that produces a modeled times series that has the best statistical match to the observations from certain AGAGE stations (e.g., Mace Head, Cape Grim). NAME was driven with output from the operational analysis of the UK Met Office Numerical Weather Prediction model, the Unified Model, at global horizontal resolution of 17–40 km (year dependent). InTEM estimates the spatial distribution of emissions across a defined geographical area, and can either start from a random emission distribution or be constrained by an inventory-defined distribution. Emission totals from specific geographical areas are calculated by summing the derived emissions from each grid (non-uniform) in that region.
The uncertainty estimation used within InTEM is described in detail elsewhere (Manning et al., 2011). Briefly, the uncertainty space was explored by (a) solving the inversion multiple times with a range of baseline mole fractions within the baseline uncertainty estimated during the baseline fitting process and (b) by altering the 3-year inversion time window by 1 month throughout the data period thereby solving over a particular 1-year period many times using different observations. In total for each annual estimate, up to 111 inversions were performed; the median and 5th and 95th percentiles were used as the final total and spread. For the Australian estimates data between 2002 and 2011 were used, for the NW European estimates data between November 1994 and December 2013 were used.
A regional Bayesian inversion system using backward simulations of a
Lagrangian particle dispersion model FLEXPART (Stohl et al., 2005) was
applied to the HFC-152a observations from Mace Head, Jungfraujoch and Mt.
Cimone for the period 2006 to 2014. The inversion technique follows the
description by Stohl et al. (2009) and was previously applied to regional
halocarbon emissions from Europe (Keller et al., 2012; Maione et al., 2014)
and China (Vollmer et al., 2009). For these emission estimates, the
background was determined by applying the Robust Extraction of Baseline
Signal (REBS) filter described in detail by Ruckstuhl et al. (2012). The
transport model FLEXPART was driven with output from the operational
analysis of the Integrated Forecast System (IFS) of the European Centre for
Medium Range Weather Forecast (ECMWF) using a spatial resolution of
0.2
The FLEXPART model was applied to the HFC-152a observations from Mace Head, Jungfraujoch and Mt. Cimone for the period 2006 to 2014. Prior to 2006, the model resolution of Integrated Forecast System (IFS) was not sufficiently fine to realistically simulate the transport to the two high altitude sites Jungfraujoch and Mt. Cimone. Therefore, no attempt was made here to apply the inversion system to years before 2006. As prior information of the HFC-152a emissions we used country totals as submitted to UNFCCC. These were spatially disaggregated following the HFC-152a distribution given in EDGAR (v4.2). For countries not reporting HFC-152a emissions to UNFCCC we used the values given in EDGAR. The EDGAR inventory was only available up to the year 2008 beyond this year the EDGAR 2008 distribution was used. The uncertainty of the prior emissions was set so that the region total uncertainty equalled 20 % of the region total emissions. The regional inversion grid covered a region similar to that shown in Fig. 1.
We also present regional emissions estimates using inter-species correlation (ISC) methods (Yokouchi et al., 2005). Emissions of a number of trace gases from the Melbourne/Port Phillip region (CFCs, HCFCs, HFCs, carbon tetrachloride: Dunse et al., 2001, 2002, 2005; O'Doherty et al., 2009; Fraser et al., 2014a, b), including HFC-152a (Greally et al., 2007), have been estimated utilizing in situ high frequency measurements from Cape Grim and ISC with co-incident carbon monoxide (CO) measurements.
ISC works best for co-located sources – however extensive modeling has shown that by the time the Melbourne/Port Phillip plume reaches Cape Grim (300 km from the source) it is well mixed and the likely inhomogeneity of the source regions (for CO and HFC-152a in this case) does not have a significant influence on the derived emissions. It should be noted that in order to obtain a significant sampling of Port Phillip pollution episodes at Cape Grim, data from 3 years (for example 2011–2013) are used to derive annual emissions (for 2012). (InTEM also uses data from 3 years to derive annual emissions.) The ISC uncertainties given in the paper include (1) the uncertainties in the estimates of CO emissions from Melbourne/Port Phillip (2) the uncertainties in the overall correlation between CO and HCFC-152a as seen in pollution episodes at Cape Grim (3) the uncertainties in the geographic extent of the HFC-152a and CO source regions impacting on Cape Grim and their entrained population.
Using HCFC-22 as the reference tracer, Li et al. (2011) reported that China
is the dominant emitter of halocarbons in East Asia. North American HFC-152a
emissions have been estimated from atmospheric data using interspecies
correlation based techniques with CO (Millet et al., 2009; Barletta et al.,
2011) and fossil fuel CO
The time series of HFC-152a in situ observations recorded at selected AGAGE and affiliated monitoring stations are shown in Fig. 2a–c. Data have been filtered into baseline (black) and above baseline (red) using the AGAGE pollution algorithm, as discussed in Sect. 2.3. Figure 2a shows the mole fractions in ppt for the four stations that deployed both ADS and Medusa GC-MS instruments (Mace Head, Zeppelin, Jungfraujoch, and Cape Grim). Most notable are the substantial above baseline events at Mace Head and Jungfraujoch that are influenced primarily by emissions from European sources. Conversely, the Zeppelin Arctic station and the SH station at Cape Grim have relatively small above baseline events implying smaller emissions from local or regional sources.
Figure 2b shows measurements at the five other AGAGE stations (Trinidad
Head, Gosan, Ragged Point, Shangdianzi, and Cape Matatula), which used only
Medusa GC-MS instruments. The North American site at Trinidad Head and the
Asian sites at Shangdianzi and Gosan are the most strongly influenced by
regional emissions. The tropical sites at Ragged Point, Barbados, and Cape
Matatula, American Samoa show very few enhancements above the baseline and
these are due mostly to local emissions occurring under nighttime inversion
conditions and occasional influences from regional emission sources (note
the different
Figure 2c illustrates the time series from the two affiliated AGAGE stations (Monte Cimone and Haturuma) that used comparable GC-MS instruments but with different methods of pre-concentration. Monte Cimone, like the Jungfraujoch, is also influenced by substantial emissions from sources in continental Europe. Hateruma is influenced by sources in China, Korea, Taiwan, and Japan (Yokouchi et al., 2006).
Figure 3 shows the in situ measurements of HFC-152a, as baseline monthly means (excluding pollution events), obtained from the two AGAGE stations Mace Head and Cape Grim with the longest time series that deployed both ADS and Medusa GC-MS instruments. Superimposed in Fig. 3 are the NH and SH archived flask data extending back to 1978. Annual average mole fractions at Mace Head increased from 1.2 ppt in 1994 to 10.2 ppt by 2014, Cape Grim annual average mole fractions increased from 0.84 ppt in 1998 when in situ measurements first began to 4.5 ppt in 2014. However, in the last few years the rates of growth at both sites have slowed to almost zero.
HFC-152a baseline monthly mean mole fraction (ppt) recorded at Mace Head-MHD (ADS GC-MS, 1994–2003; Medusa GC-MS, 2004–2014) and at Cape Grim-CGO (ADS GC-MS, 1998–2003; Medusa GC-MS, 2004–2014) and from analysis of archived NH and SH air samples extending back to 1975: in situ (black), air archive NH (red) and SH (blue).
The NH archived samples are more variable than the SH archived samples. The SH archive is collected only under strict baseline conditions (Southern Ocean air) and is far removed from the major sources of HFC-152a. Conversely in the NH, where most major sources of emissions are located, sampling under strict baseline conditions is more difficult to achieve.
Figure 4a illustrates HFC-152a baseline monthly means obtained from the five other AGAGE observing sites (Ragged Point, Gosan, Cape Matatula, Trinidad Head, and Shangdianzi using only the more advanced Medusa GC-MS. There is a large seasonal cycle at Gosan with a very deep minimum due to summertime transport from the Southern Hemisphere (Li et al., 2011). Barbados can also be influenced by Southern Hemispheric air during the hurricane season (Archibald et al., 2015).
Figure 4b shows the baseline monthly mean mole fractions for the three mountain stations. Ny-Ålesund and Jungfraujoch, using combined ADS and Medusa GC-MS measurements and Monte Cimone, which used a commercial pre-concentrator GC-MS. In most years Monte Cimone exhibits enhanced mole fractions during the NH spring months (March–May).
The HFC-152a seasonal cycles at Mace Head and Cape Grim shown in Fig. 5a and b, are broadly representative of the Northern Hemisphere and Southern Hemisphere, respectively. The seasonal cycle at Mace Head shows a NH spring maximum (April–May) and late summer minimum (August–October), while the SH seasonal cycle at Cape Grim exhibits a broad austral spring maximum (July–November) and a late summer minimum (January–April). The summer minimum at both locations is attributed to enhanced summertime loss (OH) with possibly a contribution from seasonally varying emissions in the NH that may be out-of-phase with the NH sink. At Cape Grim an additional source of seasonality is due to seasonally varying transport between the NH and SH, which is generally in phase with the sink induced seasonal cycle. This competition between OH summertime loss and seasonally varying transport has been observed at many other AGAGE locations (Prinn et al., 1992; Greally et al., 2007; O'Doherty et al., 2009, 2014; Li et al., 2011).
Figure 6 shows the mole fractions output from the AGAGE global 12-box model,
along with the monthly mean semi-hemispheric average observations used in
the inversion. The figure also shows the running mean growth rate, smoothed
using a Kolmogorov–Zurbenko filter with a window of approximately 12 months
(Rigby et al., 2014). Most notable is the positive growth rate from 1995
reaching a maximum of
Top panel: AGAGE 12-box model mole fractions (solid line) for the
two NH (30–90
The strong inter-hemispheric gradient demonstrates that emissions are
predominantly in the NH, as has been illustrated for many other purely
anthropogenic trace gases (Prinn et al., 2000). The globally averaged mole
fraction in the lower troposphere in 2014 is estimated to be 6.84
Estimated global emissions of HFC-152a using the 12-box model and the
reported UNFCCC and EDGAR emission inventories are shown in Fig. 7 and
Table 2. The blue solid line represents our model-derived emissions, with
the 1
The data shown in column 3 of Table 2 are the totals of submissions by the
national governments to the UNFCCC (Rio Convention) as reported in Table 2(II) s1 in the Common Reporting Format (CRF), available on the UNFCCC
website (
The additional component of US emissions makes a substantial contribution to
the very large difference between the UNFCCC data as reported and the
adjusted values. This is partly due to the low global warming potential of
HFC-152a (a factor of 10 lower than other HFCs) which magnifies its mass
component in the 8200 Gg CO
The AGAGE observation based global emissions are substantially higher than
the emissions calculated from the UNFCCC GHG reports (2014 submission). It
is not unreasonable that UNFCCC-reported emissions are lower than the AGAGE
global emission estimates, since countries and regions in Asia (e.g., China,
Indonesia, Korea, Malaysia, the Philippines, Taiwan, Vietnam), the Indian
sub-continent (e.g., India, Pakistan), the Middle East, South Africa, and
Latin America do not report to the UNFCCC. Where we include the HFC-152a
component of unspecified emissions (green line in Fig. 7) results are
consistent within the error bars until approximately 2003 to 2005 when they
start to diverge (UNFCCC
Lunt et al. (2015) have reported global and regional emissions estimates for the most abundant HFCs, based on inversions of atmospheric mole fraction data, aggregated into two categories; those from Annex 1 countries and those from non-Annex 1 countries. The inversion methodology used the NAME model to simulate atmospheric transport close to the monitoring sites, and the Model for Ozone and Related chemical Tracers (MOZART, Emmons et al., 2010) to simultaneously calculate the effect of changes to the global emissions field on each measurement site. The model sensitivities were combined with a prior estimate of emissions (based on EDGAR) and the atmospheric measurements, in a hierarchical Bayesian inversion (Ganesan et al., 2014), to infer emissions.
HFC-152a emissions estimates derived from observations (blue line
and shading, 1
Annex 1 and non-Annex 1 global and regional emissions in Gg yr
Using this method we infer emissions estimates for the entire world, Europe,
North America, and East Asia. Table 3 lists our estimated regional emissions
in Gg yr
The HFC-152a perturbations above baseline, observed at Mace Head, are driven by emissions on regional scales that have yet to be fully mixed on the hemisphere scale. The Mace Head observations are coupled with NAME model air history maps using the inversion system InTEM to estimate surface emissions across NWEU (Manning et al., 2011). NWEU is defined as United Kingdom, Ireland, Germany, France, Benelux, and Denmark.
As shown in Fig. 8, the NWEU emission estimates for HFC-152a from InTEM
(rolling 3-yr averages) agree to within inversion uncertainties with the
UNFCCC data (2013 submission) for most years. The estimates of NWEU
emissions grew steadily from 1995 reaching a maximum emission of 1.6
Emission (Gg yr
The temporal evolution of emission estimates for different European regions
are given in Fig. 9. In contrast to the InTEM estimates the Bayesian
inversion derived emissions in NWEU were slightly smaller than the UNFCCC
estimate and showed a continued decrease until 2014. Total emissions in the
inversion domain ranged from 4
HFC-152a emission estimates for different European regions using
the Bayesian regional inversion (orange bars) and prior estimates as
reported to UNFCCC (green bars). Error bars indicate 2
Estimates of North American emissions have been reported by several groups
(see also estimates from this study in Table 3). Millet et al. (2009) report
average US emissions for 2004–2006 of 7.6 Gg (4.8–10 Gg) compared with the
UNFCCC average 2005–2006 estimate of 12.3 Gg calculated from UNFCCC data.
Miller et al. (2012) provided HFC-152a emissions estimates averaged from
2004–2009 of 25 Gg (11–50 Gg). Barletta et al. (2011) reported a 2008
HFC-152a emission estimate of 32
If the sources of emissions from the US were solely technical aerosols and construction foam, emissions would be expected to be far lower. These were the historic uses in Europe and Japan and resulted in emissions 10 times less than those estimated for the US. However, in the US, do-it-yourself (DIY) refilling of car air conditioners is not only permitted but thriving (Zhan et al., 2014), with an estimated 24 million DIY refilling operations attempted each year. The practice is banned in Europe (OJ, 2014).
Furthermore, there is ample evidence online that HFC-152a is extensively
used in DIY refilling on account of its lower cost. It is a technically
suitable replacement for HFC-134a, although there are safety concerns of
importance to vehicle manufacturers (Hill, 2003). If the quantities
estimated by Zhan et al. (2014) were met using HFC-152a diverted from the
retail trade in technical aerosols, some 10 to 20 Gg yr
Emissions of HFC-152a from China were estimated to be 4.3
Yao et al. (2012), using the interspecies correlation method with carbon
monoxide as the reference tracer, reported more recent Chinese emissions of
2
Australian HFC-152a emissions (Mg yr
SE Australian emissions of HFC-152a are estimated using the positive enhancements above baseline or background concentrations observed at Cape Grim using interspecies correlation with CO as the reference species (ISC: Dunse et al., 2005; Greally et al., 2007) and inverse modeling (InTEM: Manning et al., 2003, 2011). Figure 2a (CGO) shows an overall increase in the magnitude of HFC-152a pollution episodes, presumably due to increasing regional emissions. Detailed analysis of these pollution episodes using air mass back trajectories shows clearly that the HFC-152a pollution seen at Cape Grim originates largely from Melbourne and the surrounding Port Phillip region.
Australian HFC-152a emissions of 5–10 Mg yr
The InTEM model (Manning et al., 2003, 2011) has been used to derive HFC-152a emissions from Victoria/Tasmania (Fraser et al., 2014a). Annual Australian emissions are calculated from Victoria/Tasmania emissions using a population based scale factor of 3.7 and are shown in Fig. 10 and the 3rd column of Table 4, interpolated from rolling 3-year emission estimates. Over the period 2002–2011, the average Australian HFC-152a emissions from ISC and InTEM agree to within 2 %. The method for estimating the InTEM uncertainties are discussed above. No additional uncertainty was applied to the estimates through the process of up-scaling from Victoria/Tasmania to Australian totals. The assumption was made that the use of HFC-152a per head of population was identical across Australia as we have no more detailed information.
Australian HFC-152a emissions have increased steadily from 25 Mg yr
Compared to the global values derived above, Australian emissions are 0.1 % of global emissions based on ISC/InTEM data. It is unusual for Australian emissions of an industrial chemical to be as low as 0.1 % of global emissions. For other HFCs, CFCs and HCFCs (for example HFC-134a, CFC-12, HCFC-22), Australian emissions as fraction of global emissions are typically 1–2 %, similar to Australia's fraction of global gross domestic product (GDP, 1.9 %, 2014) but significantly larger than Australia's fraction of global population (0.33 %, 2014) (Fraser et al., 2014b).
The possible reasons for the low Australian HFC-152a emissions (relatively low use in Australia compared to rest of world) are being investigated. One suggestion (M. Bennett, Refrigerant Reclaim Australia, personal communication, 2013) is that a significant major-volume use in other parts of the world for HFC-152a is as an aerosol propellant, a use not taken up to any significant degree in Australia.
Australian HFC-152a emissions (Mg yr
Atmospheric abundances and temporal trends of HFC-152a have been estimated from data collected at the network of 11 globally distributed monitoring sites. The longest continuous in situ record at Mace Head, Ireland covers a 20-year period from 1994–2014. Other stations within the network have observational records from 9 to 16 years, with only a short record (2010–2012) at Shangdianzi, China. From selected baseline in situ measurements and measurements of archived air samples dating back to 1978 the long-term growth rate of HFC-152a has been deduced. Analyzing the enhancements above baseline coupled with atmospheric transport models permitted us to estimate both regional and global HFC-152a emissions. However, it should be noted that the various models use different domains to obtain regional emissions estimates.
The annual average NH (Mace Head
Global HFC-152a emissions increased from 7.3
Substantial differences in emission estimates of HFC-152a were found between this study and those reported to the UNFCCC which we suggest arises from underestimated North American emissions and undeclared Asian emissions; reflecting the incomplete global reporting of GHG emissions to the UNFCCC and/or biases in the accounting methodology. Ongoing, continuous, and accurate globally and regionally distributed atmospheric measurements of GHGs, such as HFC-152a, are required for “top-down” quantification of global and regional emissions of these gases, thereby enabling improvements in national emissions inventories, or “bottom-up” emissions data collected and reported to the UNFCCC (Weiss and Prinn, 2011).
The entire ALE/GAGE/AGAGE data base comprising every calibrated measurement including pollution events is archived on the Carbon Dioxide Information and Analysis Center (CDIAC) at the US Department of Energy, Oak Ridge National Laboratory.
We specifically acknowledge the cooperation and efforts of the station operators (G. Spain, MHD; R. Dickau, THD; P. Sealy, RPB; NOAA officer-in-charge, SMO) at the AGAGE stations and all other station managers and support staff at the different monitoring sites used in this study. We particularly thank NOAA and NILU for supplying some of the archived air samples shown, allowing us to fill important gaps. The operation of the AGAGE stations was supported by the National Aeronautic and Space Administration (NASA, USA) (grants NAG5-12669, NNX07AE89G and NNX11AF17G to MIT; grants NAG5-4023, NNX07AE87G, NNX07AF09G, NNX11AF15G and NNX11AF16G to SIO); the Department of the Energy and Climate Change (DECC, UK) (contract GA0201 to the University of Bristol); the National Oceanic and Atmospheric Administration (NOAA, USA) (contract RA133R09CN0062 in addition to the operations of American Samoa station); and the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia), Bureau of Meteorology (Australia). Financial support for the Jungfraujoch measurements is acknowledged from the Swiss national programme HALCLIM (Swiss Federal Office for the Environment (FOEN)). Support for the Jungfraujoch station was provided by International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG). The measurements at Gosan, South Korea were supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2014R1A1A3051944). Financial support for the Zeppelin measurements is acknowledged from the Norwegian Environment Agency. Financial support for the Shangdianzi measurements is acknowledged from the National Nature Science Foundation of China (41030107, 41205094). The CSIRO and the Australian Government Bureau of Meteorology are thanked for their ongoing long-term support of the Cape Grim station and the Cape Grim science program. M. Rigby is supported by a NERC Advanced Fellowship NE/I021365/1.Edited by: E. Harris