16:00 〜 16:15
[AAS05-19] Seasonal variations of methane sources in South Asia inferred from observations of stable carbon isotope ratio of atmospheric methane
キーワード：methane、carbon cycle、isotope、emission inventory
Various types of methane (CH4) sources reside in South Asia, including ruminant, rice paddy, biomass burning, fossil fuel industry, and landfills. For effective reduction of CH4 emissions, observations of stable carbon isotope ratio of CH4 (δ13C-CH4) play an important role. The δ13C-CH4 data provide better understanding of CH4 emission sources because of distinct δ13C-CH4signatures for individual source types: –60‰ for microbial, –40‰ for fossil fuel, and –25‰ for biomass burning sources.
Here we present the first observations of atmospheric δ13C-CH4 at two sites in South Asia. We have performed weekly air sampling at Nainital (29.36° N, 79.46° E, 1940 m a.s.l.) in the Himalaya mountain area, northern India since 2006 and at Comilla (23.43° N, 91.18° E, 30 m a.s.l.) in the paddy area, central Bangladesh since 2012. The air samples were analyzed for atmospheric GHGs and related tracers (Nomura et al., submitted to ACP). Using a newly developed measurement system for δ13C-CH4(Umezawa et al., JMSJ, 2020), we started δ13C-CH4 analysis for air samples collected at both sites from August 2018. The observational data were grouped into three seasons for Comilla (from August to November (ASON), from December to March (DJFM) and from April to July (AMJJ)) and two seasons for Nainital (July and August (JA) and others) by considering the monsoon and agricultural cycles in the regions.
Large increases in the CH4 concentration were observed during ASON and DJFM at Comilla. The relationship between δ13C-CH4and the reciprocal of CH4 concentration (Keeling plot) at Comilla inferred that the δ13C-CH4 signature of the CH4 source was –52‰ for ASON and –45‰ for DJFM, while no significant correlation was found for AMJJ. Our result suggested that 75% of CH4emission come from microbial sources and 19% from biomass burning during ASON, while the estimated relative contributions are 55% and 39% during DJFM, by assuming that 6% of the total CH4 emission was from fossil fuels in Bangladesh (EDGAR V4.3.2). These estimates of contribution of biomass burnings to the total CH4 emission were much higher than those in the EDGAR inventory, suggesting that the EDGAR inventory underestimate the CH4 emission from biomass burnings in Bangladesh. Our observation also showed higher carbon monoxide (CO) concentrations during DJFM than ASON, consistent with the above results suggesting the large contribution of biomass burnings during DJFM. At Nainital, increases in the CH4 concentration and decreases in δ13C-CH4 were observed during JA, although the ranges of variations in the CH4 concentration and δ13C-CH4 were much smaller than those at Comilla. The δ13C-CH4 signature of the regional source was estimated to be –51‰ throughout the year, showing no seasonal variations of CH4 sources. The Nainital data might represent a typical δ13C-CH4 signature over the region.
Here we present the first observations of atmospheric δ13C-CH4 at two sites in South Asia. We have performed weekly air sampling at Nainital (29.36° N, 79.46° E, 1940 m a.s.l.) in the Himalaya mountain area, northern India since 2006 and at Comilla (23.43° N, 91.18° E, 30 m a.s.l.) in the paddy area, central Bangladesh since 2012. The air samples were analyzed for atmospheric GHGs and related tracers (Nomura et al., submitted to ACP). Using a newly developed measurement system for δ13C-CH4(Umezawa et al., JMSJ, 2020), we started δ13C-CH4 analysis for air samples collected at both sites from August 2018. The observational data were grouped into three seasons for Comilla (from August to November (ASON), from December to March (DJFM) and from April to July (AMJJ)) and two seasons for Nainital (July and August (JA) and others) by considering the monsoon and agricultural cycles in the regions.
Large increases in the CH4 concentration were observed during ASON and DJFM at Comilla. The relationship between δ13C-CH4and the reciprocal of CH4 concentration (Keeling plot) at Comilla inferred that the δ13C-CH4 signature of the CH4 source was –52‰ for ASON and –45‰ for DJFM, while no significant correlation was found for AMJJ. Our result suggested that 75% of CH4emission come from microbial sources and 19% from biomass burning during ASON, while the estimated relative contributions are 55% and 39% during DJFM, by assuming that 6% of the total CH4 emission was from fossil fuels in Bangladesh (EDGAR V4.3.2). These estimates of contribution of biomass burnings to the total CH4 emission were much higher than those in the EDGAR inventory, suggesting that the EDGAR inventory underestimate the CH4 emission from biomass burnings in Bangladesh. Our observation also showed higher carbon monoxide (CO) concentrations during DJFM than ASON, consistent with the above results suggesting the large contribution of biomass burnings during DJFM. At Nainital, increases in the CH4 concentration and decreases in δ13C-CH4 were observed during JA, although the ranges of variations in the CH4 concentration and δ13C-CH4 were much smaller than those at Comilla. The δ13C-CH4 signature of the regional source was estimated to be –51‰ throughout the year, showing no seasonal variations of CH4 sources. The Nainital data might represent a typical δ13C-CH4 signature over the region.