We present a multiyear time series of column abundances of carbon monoxide (CO), hydrogen cyanide (HCN), and ethane (C2H6) measured using Fourier-transform infrared (FTIR) spectrometers at 10 sites ...affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC). Six are high-latitude sites: Eureka, Ny-Ålesund, Thule, Kiruna, Poker Flat, and St. Petersburg, and four are midlatitude sites: Zugspitze, Jungfraujoch, Toronto, and Rikubetsu. For each site, the interannual trends and seasonal variabilities of the CO time series are accounted for, allowing background column amounts to be determined. Enhancements above the seasonal background were used to identify possible wildfire pollution events. Since the abundance of each trace gas emitted in a wildfire event is specific to the type of vegetation burned and the burning phase, correlations of CO to the long-lived wildfire tracers HCN and C2H6 allow for further confirmation of the detection of wildfire pollution. A GEOS-Chem tagged CO simulation with Global Fire Assimilation System (GFASv1.2) biomass burning emissions was used to determine the source attribution of CO concentrations at each site from 2003 to 2018. For each detected wildfire pollution event, FLEXPART back-trajectory simulations were performed to determine the transport times of the smoke plume. Accounting for the loss of each species during transport, the enhancement ratios of HCN and C2H6 with respect to CO were converted to emission ratios. We report mean emission ratios with respect to CO for HCN and C2H6 of 0.0047 and 0.0092, respectively, with a standard deviation of 0.0014 and 0.0046, respectively, determined from 23 boreal North American wildfire events. Similarly, we report mean emission ratios for HCN and C2H6 of 0.0049 and 0.0100, respectively, with a standard deviation of 0.0025 and 0.0042, respectively, determined from 39 boreal Asian wildfire events. The agreement of our emission ratios with literature values illustrates the capability of ground-based FTIR measurements to quantify biomass burning emissions. We provide a comprehensive dataset that quantifies HCN and C2H6 emission ratios from 62 wildfire pollution events. Our dataset provides novel emission ratio estimates, which are sparsely available in the published literature, particularly for boreal Asian sources.
Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and
methane (CH4) is the second most important greenhouse gas contributing to human-induced
climate change. ...Detailed and continuous observations of these gases are necessary to better assess
their impact on climate and atmospheric pollution. While surface and airborne measurements are able
to accurately determine atmospheric abundances on local scales, global coverage can only be
achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which
was successfully launched in October 2017, is a spaceborne nadir-viewing imaging spectrometer
measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath
on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the
shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These
characteristics enable the determination of both gases with an unprecedented level of detail on a
global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4
are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 µm
spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm
Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). This algorithm
is intended to be used with the operational algorithms for mutual verification and to provide new
geophysical insights. We introduce the algorithm in detail, including expected error characteristics
based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration
procedure applied in the post-processing of the XCH4 data. The quality of the results based
on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer
(FTS) measurements providing realistic error estimates of the satellite data: the XCO data
set is characterised by a random error of 5.1 ppb (5.8 %) and a systematic error of
1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb
(0.8 %) and a systematic error of 4.3 ppb (0.2 %). The natural XCO and
XCH4 variations are well-captured by the satellite retrievals, which is demonstrated by a
high correlation with the validation data (R=0.97 for XCO and R=0.91 for XCH4 based
on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison
to the operational products and examples of the detection of emission sources in a single satellite
overpass, such as CO emissions from the steel industry and CH4 emissions from the
energy sector, which potentially allows for the advance of emission monitoring and air quality assessments
to an entirely new level.
The dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO
emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into ...the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH
yr
) of the methane emission growth rate of China (0.87 Tg CH
yr
) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.
Global Observation of Greenhouse Gases by GOSAT Series UCHINO, Osamu; OHYAMA, Hirofumi; MORINO, Isamu ...
Journal of The Society of Instrument and Control Engineers,
2022/05/10, Letnik:
61, Številka:
5
Journal Article
We analyzed seasonality and interannual variability of tropospheric hydrogen cyanide (HCN)
columns in densely populated eastern China for the first time. The results
were derived from solar ...absorption spectra recorded with a ground-based high-spectral-resolution Fourier transform infrared (FTIR) spectrometer in Hefei
(31∘54′ N, 117∘10′ E) between 2015 and
2018. The tropospheric HCN columns over Hefei, China, showed significant
seasonal variations with three monthly mean peaks throughout the year. The
magnitude of the tropospheric HCN column peaked in May, September, and December. The tropospheric HCN column reached a maximum monthly
mean of (9.8±0.78)×1015 molecules cm−2 in May
and a minimum monthly mean of (7.16±0.75)×1015 molecules cm−2 in November. In most cases, the tropospheric HCN columns
in Hefei (32∘ N) are higher than the FTIR observations in Ny-Ålesund (79∘ N), Kiruna (68∘ N), Bremen (53∘ N), Jungfraujoch (47∘ N), Toronto (44∘ N), Rikubetsu
(43∘ N), Izana (28∘ N), Mauna Loa (20∘ N), La
Reunion Maido (21∘ S), Lauder (45∘ S), and Arrival
Heights (78∘ S) that are affiliated with the Network for Detection
of Atmospheric Composition Change (NDACC). Enhancements of tropospheric HCN
column were observed between September 2015 and July 2016 compared to the
same period of measurements in other years. The magnitude of the enhancement
ranges from 5 % to 46 % with an average of 22 %. Enhancement of
tropospheric HCN (ΔHCN) is correlated with the concurrent
enhancement of tropospheric CO (ΔCO), indicating that enhancements
of tropospheric CO and HCN were due to the same sources. The GEOS-Chem tagged CO simulation, the global fire maps, and the potential source
contribution function (PSCF) values calculated using back trajectories
revealed that the seasonal maxima in May are largely due to the influence of
biomass burning in Southeast Asia (SEAS) (41±13.1 %), Europe
and boreal Asia (EUBA) (21±9.3 %), and Africa (AF) (22±4.7 %). The seasonal maxima in September are largely due to the influence
of biomass burnings in EUBA (38±11.3 %), AF (26±6.7 %),
SEAS (14±3.3 %), and North America (NA) (13.8±8.4 %).
For the seasonal maxima in December, dominant contributions are from AF (36±7.1 %), EUBA (21±5.2 %), and NA (18.7±5.2 %). The tropospheric HCN enhancement between September 2015 and July
2016 at Hefei (32∘ N) was attributed to an elevated influence of
biomass burnings in SEAS, EUBA, and Oceania (OCE) in this period. In
particular, an elevated number of fires in OCE in the second half of 2015
dominated the tropospheric HCN enhancement between September and December 2015. An
elevated number of fires in SEAS in the first half of 2016 dominated the
tropospheric HCN enhancement between January and July 2016.
In Asia, much effort is put into reducing methane (CH4) emissions due to the region's contribution to the recent rapid global atmospheric CH4 concentration growth. Accurate quantification of Asia's ...CH4 budgets is critical for conducting global stocktake and achieving the long-term temperature goal of the Paris Agreement. In this study, we present top-down estimates of CH4 emissions from 2009 to 2018 deduced from atmospheric observations from surface network and GOSAT satellite with the high-resolution global inverse model NIES-TM-FLEXPART-VAR. The optimized average CH4 budgets are 63.40 ± 10.52 Tg y−1 from East Asia (EA), 45.20 ± 6.22 Tg y−1 from Southeast Asia (SEA), and 64.35 ± 9.28 Tg y−1 from South Asia (SA) within the 10 years. We analyzed two 5 years CH4 emission budgets for three subregions and 13 top-emitting countries with an emission budget larger than 1 Tg y−1, and interannual variabilities for these subregions. Statistically significant increasing trends in emissions are found in EA with a lower emission growth rate during 2014-2018 compared to that during 2009-2013, while trends in SEA are not significant. In contrast to the prior emission, the posterior emission shows a significant decreasing trend in SA. The flux decrease is associated with the transition from strong La Ninña (2010-2011) to strong El Ninño (2015-2016) events, which modulate the surface air temperature and rainfall patterns. The interannual variability in CH4 flux anomalies was larger in SA compared to EA and SEA. The Southern Oscillation Index correlates strongly with interannual CH4 flux anomalies for SA. Our findings suggest that the interannual variability in the total CH4 flux is dominated by climate variability in SA. The contribution of climate variability driving interannual variability in natural and anthropogenic CH4 emissions should be further quantified, especially for tropical countries. Accounting for climate variability may be necessary to improve anthropogenic emission inventories.
As an important greenhouse gas (GHG) in the atmosphere, carbon dioxide (CO 2 ) has a great impact on global climate change. Accurate knowledge of the spatiotemporal variations of CO 2 is of great ...significance for understanding the carbon cycle and evaluating the effectiveness of carbon emission reduction. In recent years, several satellites with CO 2 sensors have been launched and a series of atmospheric CO 2 concentration products have been developed using different retrieval algorithms. This study validated nine satellite XCO 2 products derived from Greenhouse gases Observing SATellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory-2 (OCO-2), and OCO-3: including ACOS-GOSAT, NIES-GOSAT, BESD-GOSAT, OCFP-GOSAT, SRFP-GOSAT, EMMA, GOSAT-2, OCO-2, and OCO-3 XCO 2 . The remotely sensed XCO 2 products were compared with the XCO 2 observations from six Total Carbon Column Observing Network (TCCON) stations in East Asia for validation. The results showed that the OCO-2 XCO 2 product outperformed other products, with the highest R 2 of 0.94 and the lowest MAE of 1.24 ppm. The ACOS-GOSAT and EMMA-GOSAT XCO 2 products also showed favorable accuracies, both achieving R 2 of 0.93 and corresponding MAE values of 1.29 and 1.31 ppm, respectively. The GOSAT-2 XCO 2 product showed the poorest accuracy, with an R 2 of 0.77 and a mean absolute error of 3.28 ppm. There was a significant overestimation of the bias-uncorrected GOSAT-2 XCO 2 product in East Asia, and it indicated that bias correction must be performed for this XCO 2 product. The accuracy of TCCON XCO 2 was not consistent with remotely sensed XCO 2 at different stations. The RJ, JS, AN, and TK TCCON stations generally showed better agreements between satellite estimates and TCCON observations, except for the GOSAT-2 XCO 2 product.
Changes of atmospheric methane total columns (CH4) since 2005 have been evaluated using Fourier transform infrared (FTIR) solar observations carried out at 10 ground-based sites, affiliated to the ...Network for Detection of Atmospheric Composition Change (NDACC). From this, we find an increase of atmospheric methane total columns of 0.31 ± 0.03 % year−1 (2σ level of uncertainty) for the 2005–2014 period. Comparisons with in situ methane measurements at both local and global scales show good agreement. We used the GEOS-Chem chemical transport model tagged simulation, which accounts for the contribution of each emission source and one sink in the total methane, simulated over 2005–2012. After regridding according to NDACC vertical layering using a conservative regridding scheme and smoothing by convolving with respective FTIR seasonal averaging kernels, the GEOS-Chem simulation shows an increase of atmospheric methane total columns of 0.35 ± 0.03 % year−1 between 2005 and 2012, which is in agreement with NDACC measurements over the same time period (0.30 ± 0.04 % year−1, averaged over 10 stations). Analysis of the GEOS-Chem-tagged simulation allows us to quantify the contribution of each tracer to the global methane change since 2005. We find that natural sources such as wetlands and biomass burning contribute to the interannual variability of methane. However, anthropogenic emissions, such as coal mining, and gas and oil transport and exploration, which are mainly emitted in the Northern Hemisphere and act as secondary contributors to the global budget of methane, have played a major role in the increase of atmospheric methane observed since 2005. Based on the GEOS-Chem-tagged simulation, we discuss possible cause(s) for the increase of methane since 2005, which is still unexplained.
A 1.6 μm differential absorption Lidar (DIAL) system for measurement of vertical CO₂ mixing ratio profiles has been developed. A comparison of CO₂ vertical profiles measured by the DIAL system and an ...aircraft in situ sensor in January 2014 over the National Institute for Environmental Studies (NIES) in Tsukuba, Japan, is presented. The DIAL measurement was obtained at an altitude range of between 1.56 and 3.60 km with a vertical resolution of 236 m (below 3 km) and 590 m (above 3 km) at an average error of 1.93 ppm. An in situ sensor for cavity ring-down spectroscopy of CO₂ was installed in an aircraft. CO₂ mixing ratio measured by DIAL and the aircraft sensor ranged from 398.73 to 401.36 ppm and from 399.08 to 401.83 ppm, respectively, with an average difference of -0.94 ± 1.91 ppm below 3 km and -0.70 ± 1.98 ppm above 3 km between the two measurements.
PDF
