The influence of forest fires on CO, HCN, C2H6, and C2H2 over northern Japan measured by infrared solar spectroscopy

Nagahama, Yoshihiro; Suzuki, Katsuhisa

doi:10.1016/j.atmosenv.2007.08.043

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…Measurements from two Japanese stations, Rikubetsu and Moshiri, were provided by the Solar-Terrestrial Environment Laboratory (STEL) of Nagoya University in Japan. A description of both Japanese sites and analysis of the measured CO columns for the period 1997-2005 can be found in Nagahama and Suzuki (2007).…”

Section: Ground-based Datamentioning

confidence: 99%

Validation of five years (2003–2007) of SCIAMACHY CO total column measurements using ground-based spectrometer observations

et al. 2010

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This paper presents a validation study of SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) carbon monoxide (CO) total column measurements from the Iterative Maximum Likelihood Method (IMLM) algorithm using ground-based spectrometer observations from twenty surface stations for the five year time period of 2003–2007.

Overall we find a good agreement between SCIAMACHY and ground-based observations for both mean values as well as seasonal variations.

For high-latitude Northern Hemisphere stations absolute differences between SCIAMACHY and ground-based measurements are close to or fall within the SCIAMACHY CO 2σ precision of 0.2 × 10¹⁸ molecules/cm² (∼10%) indicating that SCIAMACHY can observe CO accurately at high Northern Hemisphere latitudes.

For Northern Hemisphere mid-latitude stations the validation is complicated due to the vicinity of emission sources for almost all stations, leading to higher ground-based measurements compared to SCIAMACHY CO within its typical sampling area of 8° × 8°.

Comparisons with Northern Hemisphere mountain stations are hampered by elevation effects. After accounting for these effects, the validation provides satisfactory results.

At Southern Hemisphere mid- to high latitudes SCIAMACHY is systematically lower than the ground-based measurements for 2003 and 2004, but for 2005 and later years the differences between SCIAMACHY and ground-based measurements fall within the SCIAMACHY precision. The 2003–2004 bias is consistent with previously reported results although its origin remains under investigation.

No other systematic spatial or temporal biases could be identified based on the validation presented in this paper.

Validation results are robust with regard to the choices of the instrument-noise error filter, sampling area, and time averaging required for the validation of SCIAMACHY CO total column measurements.

Finally, our results show that the spatial coverage of the ground-based measurements available for the validation of the 2003–2007 SCIAMACHY CO columns is sub-optimal for validation purposes, and that the recent and ongoing expansion of the ground-based network by carefully selecting new locations may be very beneficial for SCIAMACHY CO and other satellite trace gas measurements validation efforts

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Section: Ground-based Datamentioning

confidence: 99%

Validation of five years (2003–2007) of SCIAMACHY CO total column measurements using ground-based spectrometer observations

et al. 2010

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“…Lupu et al: HCN in the UT . Having a relatively simple behaviour and a tropospheric lifetime of several months, HCN is considered to be a good tracer of biomass burning (e.g., Rinsland et al, 1999Rinsland et al, , 2000Li et al, 2003;Yokelson et al, 2003;Rinsland et al, 2007;Nagahama and Suzuki, 2007).…”

mentioning

confidence: 99%

Hydrogen cyanide in the upper troposphere: GEM-AQ simulation and comparison with ACE-FTS observations

et al. 2009

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“…The principle pathway for HCN sink is ocean uptake which accounts for 0.73 to 1.0 Tg N/year (Li et al, 2009). Additional minor sinks of HCN are attributed to atmospheric reaction with hydroxyl radical (OH) and O( 1 D), and photolysis (Li et al, 2000;Nagahama and Suzuki, 2007). The life time of HCN is 2 -5 months in the troposphere and several years in the stratosphere.…”

Section: Introductionmentioning

confidence: 99%

FTIR time series of tropospheric HCN in eastern China: seasonality, interannual variability and source attribution

Sun

Liu

Zhang

et al. 2020

Preprint

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Abstract. We analyzed seasonality and interannual variability of tropospheric HCN column amounts in densely populated eastern China for the first time. The results were derived from solar absorption spectra recorded with ground-based high spectral resolution Fourier transform infrared (FTIR) spectrometer at Hefei (117&#176;10&#8242;&#8201;E, 31&#176;54&#8242;&#8201;N) 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 peak in May&#8201;>&#8201;September&#8201;>&#8201;December. The tropospheric HCN column reached a maximum of (9.8&#8201;&#177;&#8201;0.78)&#8201;&#215;&#8201;1015&#8201;molecules/cm2 in May and a minimum of (7.16&#8201;&#177;&#8201;0.75)&#8201;&#215;&#8201;1015&#8201;molecules/cm2 in November. In most cases, the tropospheric HCN columns at Hefei (32&#176;&#8201;N) are higher than the FTIR observations at Ny Alesund (79&#176;&#8201;N), Kiruna (68&#176;&#8201;N), Bremen (53&#176;&#8201;N), Jungfraujoch (47&#176;&#8201;N), Toronto (44&#176;&#8201;N), Rikubetsu (43&#176;&#8201;N), Izana (28&#176;&#8201;N), Mauna Loa (20&#176;&#8201;N), La Reunion Maido (21&#176;&#8201;S), Lauder (45&#176;&#8201;S), and Arrival Heights (78&#176;&#8201;S) that are affiliated with the Network for Detection of Atmospheric Composition Change (NDACC). Enhancements of the tropospheric HCN columns were observed between September 2015 and July 2016 compared to the counterpart measurements in other years. The magnitude of the enhancement ranges from 5 to 46&#8201;% with an average of 22&#8201;%. Enhancement of tropospheric HCN (&#916;HCN) is correlated with the coincident enhancement of tropospheric CO (&#916;CO), 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 PSCFs (Potential Source Contribution Function) calculated using back trajectories revealed that the seasonal maxima in May is largely due to the influence of biomass burning in South Eastern Asia (SEAS) (41&#8201;&#177;&#8201;13.1&#8201;%), Europe and Boreal Asia (EUBA) (21&#8201;&#177;&#8201;9.3&#8201;%) and Africa (AF) (22&#8201;&#177;&#8201;4.7&#8201;%). The seasonal maxima in September is largely due to the influence of biomass burnings in EUBA (38&#8201;&#177;&#8201;11.3&#8201;%), AF (26&#8201;&#177;&#8201;6.7&#8201;%), SEAS (14&#8201;&#177;&#8201;3.3&#8201;%), and Northern America (NA) (13.8&#8201;&#177;&#8201;8.4&#8201;%). For the seasonal maxima in December, dominant contributions are from AF (36&#8201;&#177;&#8201;7.1&#8201;%), EUBA (21&#8201;&#177;&#8201;5.2&#8201;%), and NA (18.7&#8201;&#177;&#8201;5.2&#8201;%). The tropospheric HCN enhancement between September 2015 and July 2016 at Hefei (32&#176;&#8201;N) were attributed to an elevated influence of biomass burnings in SEAS, EUBA, and Oceania (OCE) in this period. Particularly, an elevated fire number in OCE in the second half of 2015 dominated the tropospheric HCN enhancement in September&#8211;December 2015. An elevated fire number in SEAS in the first half of 2016 dominated the tropospheric HCN enhancement in January&#8211;July 2016.

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The influence of forest fires on CO, HCN, C2H6, and C2H2 over northern Japan measured by infrared solar spectroscopy

Cited by 14 publications

References 16 publications

Validation of five years (2003–2007) of SCIAMACHY CO total column measurements using ground-based spectrometer observations

Validation of five years (2003–2007) of SCIAMACHY CO total column measurements using ground-based spectrometer observations

Hydrogen cyanide in the upper troposphere: GEM-AQ simulation and comparison with ACE-FTS observations

FTIR time series of tropospheric HCN in eastern China: seasonality, interannual variability and source attribution

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