Introduction

Atmospheric CH4 is an important component of short-lived climate pollutants that contribute both directly and indirectly to radiative forcing. It is also known that CH4 contributes to maintaining the background levels of tropospheric ozone1. CH4 is emitted from a variety of natural (e.g., wetlands, oceans, termites and clathrates) and anthropogenic (e.g., fossil-fuel exploitation, ruminant animals, rice cultivation, waste management and biomass burning) sources. Because of the shorter atmospheric lifetime (about nine years2) of CH4 than CO2, a reduction of anthropogenic emissions of CH4 would be an effective means of abating global warming in the near future3,4. However, to establish strategies for the mitigation of global warming, a quantitative understanding of the global CH4 budget is required.

Atmospheric abundance of CH4 has been increasing from pre-industrial levels of about 700 nmol mol−1 (hereafter referred to as ppb) with large year-to-year fluctuations in its growth rate5. Among the many studies that have investigated the distribution and temporal variation of CH4, several have reported conflicting results6,7,8,9. Some recent studies have suggested the existence of previously unrecognized sources of CH4. For example, satellite observations have been combined with inverse modeling techniques using CH4 retrievals from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) to provide a global distribution map of CH4 that suggests there are many emission hot spots in areas where surface observations are scarce10. The first airborne in situ measurements of CH4 over the Amazon region during the BARCA (Balanço Atmosphérico Regional de Carbono na Amazônia) campaign revealed strong CH4 emissions from the Amazonian wetlands11. Regular aircraft observations from the CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) program suggest strong biogenic emissions from India that cannot be attributed solely to rice cultivation12. These studies show that our current understanding regarding the sources of CH4 emissions is inadequate and that greater effort is needed to obtain better knowledge both of the strength of CH4 emissions and of the distribution of the sources. For this purpose, a more systematic approach is required regarding the acquisition of CH4 observations in areas where observational data are sporadic or sparse.

Since 1992, the National Institute for Environmental Studies (NIES) has conducted a voluntary observing ships (VOS) program of long-term atmospheric monitoring of climatically important trace gases over the Pacific Ocean13,14. In the Southeast Asian region, atmospheric monitoring as part of the NIES-VOS program commenced in September 2007. Although flask sampling was initially used for CH4 monitoring, since 2009, the program has been augmented by the use of continuous measurements that capture the highly variable features of CH4 in the regionally polluted air in Southeast Asia. In this paper, we present the first results of the high-resolution continuous onboard measurements of CH4 in the marine boundary layer (MBL) in the Southeast Asian region between September 2009 and April 2012. We focus on the CH4 distribution in the northern equatorial region, where strong CH4 peaks were observed off the east coast of the Malay Peninsula and the northwest coast of Borneo and we examine the emission sources responsible for these observed CH4 peaks.

Results

Although shipping operations were disrupted temporarily by the global financial crisis and the 2011 Great East Japan Earthquake, we were able to undertake observations during eight voyages between September 2009 and April 2012. Many CH4 peaks were observed in the northern equatorial region along Southeast Asian routes (Figure 1); see Methods section for further details. The locations at which these peaks occurred were concentrated in two areas: off the east coast of the Malay Peninsula (39 peaks) and off the northwest coast of Borneo (55 peaks); they are referred to hereafter as the Malay and Borneo peaks, respectively. The Malay peaks were observed largely between latitudes 8°N and 5.5°N along both the northbound and southbound routes, while the Borneo peaks were observed between latitudes 6.5°N and 4.5°N along the northbound Borneo route. Although the durations of all observed CH4 peaks were short, between several minutes to one hour, the increases of the mole fraction of CH4 were considerable, i.e., up to about 1100 ppb above the baseline levels for the Southeast Asian region. Concurrent with the CH4 peaks, we observed simultaneous CO2 peaks and the positive correlation between these CO2 and CH4 mole fractions suggested a common local, non-biogenic emission source for these gases.

Figure 1
figure 1

Latitudinal distribution of 1-min temporally averaged CH4 mole fractions between latitude 10°N and the equator observed during eight voyages along the Southeast Asian shipping routes between September 2009 and April 2012.

Mole fractions of CH4 are color coded according to the shipping routes: green for the southbound Asia route, blue for northbound Asia route and red for northbound Borneo route. Peak numbers are allocated for only the Malay and Borneo peaks (preceded by M or B) that showed substantial CH4 increase (>50 ppb) and positive correlation between CH4 and CO2 (R > 0.4, p-value < 0.05) and that had peak durations of more than 10 min.

To identify sources of CH4 emission, we examined satellite-observed night-light data from the US Air Force Defense Meteorological Satellite Project Operational Linescan System (DMSP/OLS), provided by the US National Oceanic and Atmosphere Administration15. Using the nighttime lights data “avg lights x pct”, which are annual composite images of noise-filtered nighttime lights data used to infer gas-flaring volumes16, we identified the locations of offshore platforms within the study area. The distribution of these identified offshore platforms remained largely unchanged throughout our study period. Most of these platforms were either off the east coast of the Malay Peninsula or off the northwest coast of Borneo and were near the locations of the CH4 peaks along the Southeast Asian trade routes (Figure 2). Generally, CH4 is a dominant component of emissions from offshore oil platforms, released as a result of gas flaring and venting, equipment leaks and evaporation losses, with concomitant emissions of CO2 mainly due to gas flaring16,17. These results suggest that the observed CH4 peaks represent emissions from offshore production platforms.

Figure 2
figure 2

Distribution of CH4 peaks observed during this study and offshore platforms off the east coast of the Malay Peninsula (left) and the northwest coast of Borneo (right).

Crosses are the locations where CH4 peaks were observed and their colors explain wind patterns when each CH4 peak was observed (blue: northeasterly wind; orange: southwesterly wind). Numbered peaks in Figure 1 are emphasized as large crosses, while other marginal peaks are shown by small crosses. Open red and green circles are the locations of offshore platforms in 2010, identified based on DMSP/OLS data and as reported in the EDGAR v.4.2 FT2010 database, respectively. Gray solid lines mark the routes of the VOS ships. The maps used in this figure were generated by Generic Mapping Tools (https://www.soest.hawaii.edu/gmt/).

The CH4 emissions from offshore platforms are reported in the anthropogenic trace gas emission inventory database EDGAR (Emission Database for Global Atmospheric Research) v.4.2 FT201018. We compared the distribution of offshore platforms identified in this study to that reported in EDGAR. For the comparison, we used the annual composite image for 2010 from the DMSP/OLS data and annual CH4 emission data for 2010 from EDGAR. This revealed considerable discrepancy in the distribution of offshore platforms, especially off the east coast of the Malay Peninsula, which indicates that the current emission inventories of offshore platforms in Southeast Asia still include considerable uncertainties regarding CH4 and other co-emitted gas components.

Most of our observations were performed during the boreal fall and winter season when strong northeasterly winds associated with the East Asian monsoon prevail off the east coast of the Malay Peninsula; westerly winds passing over the Malay Peninsula from the Indian Ocean prevailed only during September and October of 2009 during our observations. In contrast, there was no prevailing wind direction off the northwest coastal region of Borneo during our study period. Thus, the Malay peaks observed during the northeasterly wind season should represent emissions from offshore platforms windward of the peak locations, whereas the Borneo peaks represent emissions from both offshore platforms and onshore coastal sources. To characterize the offshore platform emissions, we examined the emissions measured during the CH4 peaks based on the CH4–CO2 enhancement ratio (ΔCH4/ΔCO2), which is the linear slope of the correlation of the mole fractions of CH4 and CO2. The observed enhancement ratio can often be used to identify the emission sources because it can be approximated to the emission ratio when observations are performed near the emission sources. For example, past observations at remote sites during wintertime have shown the ΔCH4/ΔCO2 ratios are typically less than about 20 ppb/ppm (ppm is defined as μmol mol−1) in air masses polluted principally by anthropogenic combustion-related emissions in urban and industrialized areas19,20,21,22. The emission factors of CO2 and CH4 from biomass-burning sources were determined the typical CH4/CO2 ratios to be less than 20 ppb/ppm23. These results provide diagnostic criteria for estimating the contributions from these anthropogenic emissions on land.

For further analysis of the ΔCH4/ΔCO2 ratios observed here, we selected 11 distinct Malay peaks and 16 Borneo peaks that showed substantial CH4 increases (>50 ppb) for more than 10 min and significant positive correlations between CH4 and CO2 (R > 0.4, p-value < 0.05). The ΔCH4/ΔCO2 ratios during these peaks, calculated by reduced major-axis regression24, ranged from 8 to 1108 ppb/ppm and from 3 to 880 ppb/ppm for the Malay and Borneo peaks, respectively (Figure 3). The M1, M3, M4, M5, M6, M8 and M9 peaks show similar ΔCO2-vs.-ΔCH4 correlative behavior, suggesting that they originated from the same emission process at the offshore platforms. We examined the approximate flaring efficiency (i.e., ΔCO2/(ΔCO2 + ΔCH4) in %) using the ΔCO2-vs.-(ΔCO2 + ΔCH4) regression for these peaks. The mean ΔCH4/ΔCO2 ratio for these peaks is 94 ppb/ppm, corresponding to a flaring efficiency of 92%. As gas-flaring efficiencies for industrial flares are usually greater than 90%, it is considered that these plumes originated principally from flaring with the contribution from fugitive emissions (inclusive of venting), if any, being relatively small. The M7, M10 and M11 peaks show higher ΔCH4/ΔCO2 ratios than those of the gas-flaring plumes, suggesting greater contribution from fugitive emissions. These results indicate that the observed ΔCH4/ΔCO2 ratios can vary widely, depending on the contributions from fugitive emissions. In contrast, the contributions from flaring and fugitive emissions to the M2 peak, associated with the ΔCH4/ΔCO2 ratio of 8 ppb/ppm, appear negligible. Similarly, the 16 peaks observed in the Borneo area with ΔCH4/ΔCO2 ratios higher than 20 ppb/ppm were explained by the mixing of flaring and fugitive emissions. Consequently, we chose those peaks with ΔCH4/ΔCO2 ratios higher than 20 ppb/ppm for further analysis.

Figure 3
figure 3

Scatter plots of CH4 versus CO2 mole fractions during observed CH4 peaks.

Numbering of peaks is as described in Figure 1. The left and right panels are for the Malay and Borneo peaks, respectively. Dashed lines indicate regression lines for individual peaks determined by reduced major-axis regression. The black dashed line labeled “land(20)” indicates the upper limit of the CH4–CO2 emission ratio for onshore anthropogenic emissions.

Discussion

We used these observed CH4 peaks to estimate the CH4 emission rates based on a mass balance approach25,26,27. Assuming that the CH4 plume was formed steadily during the observation and that the CH4 mixing ratio was vertically well mixed in the MBL, the CH4 emission rate qCH4 can be expressed by:

In equation (1), u is the mean horizontal wind speed along the plume axis, α is the angle between the ship transect and the perpendicular to the plume axis, ZMBL is the depth of the MBL, n is the average molar density of air within the MBL, y is the distance from the plume axis, fCH4(y) is the observed CH4 mole fraction at y and C0(y) is the background CH4 mole fraction at y. The start and end points of the integration interval for the individual CH4 peaks, a and b in equation (1), are determined manually by visual inspection and the values of C0(y) are determined practically by linear interpolation between the CH4 mole fractions at points a and b. As no meteorological observations were performed onboard, the mean wind speeds and directions and the depths of the MBL were estimated based on the CGER/METEX three-dimensional kinematic trajectory model28. This trajectory model was driven by six-hourly meteorological input data from the NCEP/NCAR reanalysis, which has a spatial resolution of 2.5° × 2.5°. The model calculation was initiated at an altitude of 250 m above sea level at the locations of the CH4 peaks. For every plume calculation, we adopted the molar density of air (n) of 1.2 kg m−3, which was the average value of n at 0 and 0.5 km29. We applied the mass balance approach to the appropriate 14 peaks that the ship transited straight across the CH4 peak. Geographical relationships between the observed CH4 peaks and the offshore platforms were well explained by the trajectory model. The resultant CH4 emission rates for the eight Malay platform peaks: M1, M3, M4, M5, M6, M7, M8 and M9 are within the range of 3.9–426.7 g s−1 with a median (mean) of 99.2 (124.7) g s−1. The rates for the six Borneo platform peaks: B1, B6, B11, B12, B13 and B14 are within the range of 1.8–46.0 g s−1 with a median (mean) of 14.7 (16.9) g s−1, as summarized in Table 1, together with the values of the parameters used in the calculations. These estimates are comparable with the recent preliminary estimates using the Visible Infrared Imaging Radiometer Suite on board the Suomi National Polar-orbiting Partnership satellite, provided by the National Geophysical Data Center of the National Oceanic and Atmospheric Administration30. As conservative error estimates, we evaluated uncertainty ranges for the emission rates by assuming the relative uncertainty of ±50% for the two dominant factors (u × cosθ and ZMBL) and ±0.05 kg m−3 for the molar density of air (1.2 kg m−3). The calculated uncertainty ranges (from lower to upper limits), also listed in Table 1, suggest substantial uncertainty in this approach.

Table 1 Estimated CH4 emission rates for the observed CH4 peaks

The median value of all the CH4 emission rates is 29.2 g s−1. Using the DMSP satellite, we identified 112 offshore platforms in the Southeast Asian region (defined as: 15°N–10°S, 90°–140°E); thus, the resultant regional total emission rate is calculated as 3.3 kg s−1. The total regional annual emission of CH4 from offshore platforms in the Southeast Asian region is estimated to be about 0.1 Tg y−1, associated with an uncertainty range of 0.02–0.32 Tg y−1 (the median values of the lower and upper limits). EDGAR reports that annual CH4 emissions from oil and gas production in 12 Southeast Asian countries (Brunei, Cambodia, Lao, Myanmar, Malaysia, the Philippines, Singapore, Thailand, Timor, Vietnam, Indonesia and Papua New Guinea) were about 3.7 Tg y−1 for 2010, which corresponds to about 1% of the 335 Tg y−1 global anthropogenic CH4 emissions18. Offshore CH4 emissions account for about 8% (0.29 Tg y−1) of the total emissions from oil and gas production in the Southeast Asian region. Despite the large uncertainty inherent in the mass balance approach, our estimate displays relatively good agreement with that by EDGAR. However, we note substantial differences in the locations of the offshore platforms between the EDGAR inventory and those determined by DMSP satellite observation. The distributions of point sources of CH4 are an important uncertainty in the existing inventories. The relative contributions of the offshore CH4 emissions to the regional CH4 emissions in Southeast Asia are estimated to be about 3% for the oil and gas production sector (both offshore and onshore) (3.7 Tg y−1, as estimated by EDGAR) and about 0.2% for the anthropogenic sources (63 Tg y−1, as estimated by EDGAR).

The global CH4 emissions in 2011 are estimated to be 556 ± 56 Tg y−1, with contributions from natural and anthropogenic emissions being comparable31. Natural emissions from wetlands are the single most dominant contributor to the total CH4 emissions, with the annual emissions being approximately 200 Tg y−1. The middle-class sources with the annual emissions in the range of 10–100 Tg y−1 include fossil fuels, ruminants, landfills/waste, geological sources, freshwater, rice paddies, burning of biomass/biofuels, wild animals and termites. The emissions from other sources are minor. The emissions (and range) from hydrates, wildfires and permafrost are estimated to be 6 (2–9), 3 (1–5) and 1 (0–1) Tg y−1, respectively.

Globally, there are a number of offshore fields for oil and gas production. In addition to Southeast Asia, the North Sea, Persian Gulf, Gulf of Guinea and Gulf of Mexico are known to be active in oil and gas production. As noted above, our estimate and EDGAR are in relatively good agreement for offshore CH4 emissions in Southeast Asia. A simple global estimate based on EDGAR implies that CH4 emissions from worldwide offshore oil and gas platforms are 1–2 Tg y−1, suggesting that the emissions from offshore sources may be comparable to those from minor natural sources such as wildfires and permafrost.

To our knowledge, this work marks the first top-down constraint on CH4 emissions from oil and gas platforms in Southeast Asia. On the other hand, we also realize the considerable uncertainty in our estimates, which derive from a combination of features inherent in the mass balance approach and the lack of samples of CH4 plumes from offshore platforms, due to the sporadic occurrence of gas flaring and fugitive emissions at oil and gas platforms. Hence, our top-down estimates of CH4 emissions from offshore platforms located in the Southeast Asian region need to be tested and improved. For example, if fugitive plumes were undersampled in our observations, the estimated CH4 emissions would be much greater, possibly even by one order of magnitude. To better assess the regional total emissions of CH4 from offshore platforms and thereby improve the current emissions inventory, further top-down constraints by integrated ship, aircraft and satellite observations are needed. In particular, the detection of fugitive plumes would be useful to reduce uncertainties in estimating the emissions. Current estimates of CH4 emissions from oil and gas processes were approximately 20% of worldwide anthropogenic emissions in 2010 and they are expected to increase by nearly 35% between 2010 and 202032. The feedback gained from plume observations can help in the reduction of fugitive emissions in Southeast Asian countries and thus, contribute to the mitigation of global warming.

Methods

We used two commercial cargo vessels in the VOS program in Southeast Asia: the M/V Fujitrans World (owned by the Kagoshima Senpaku Kaisya, Ltd., Japan) was the primary vessel with backup provided by the M/V Trans Future 1 (owned by the Toyofuji Shipping Co. Ltd., Japan). These ships regularly sail the trade routes between Japan and Southeast Asia, berthing at Osaka, Yokohama and Nagoya (Japan); Hong Kong (China); Laem Chabang (Thailand); Singapore; Port Klang, Kuching and Kota Kinabalu (Malaysia); Jakarta (Indonesia); and Muara (Brunei) at four-week intervals (Figure 4). Two northbound routes are used from Jakarta to Japan: one via Thailand and the Philippines (the northbound Asia route) and the other via Borneo (the northbound Borneo route). Only one southbound route is used from Japan to Indonesia.

Figure 4
figure 4

The VOS shipping routes in Southeast Asia.

Green and blue lines show the southbound (Japan–Indonesia) and northbound (Indonesia–Japan) Asia routes respectively; a red line shows the northbound Borneo route (Indonesia–Japan). Regular berthing ports are shown as solid black circles. The maps used in this figure were generated by Generic Mapping Tools (https://www.soest.hawaii.edu/gmt/).

Onboard each VOS ship, continuous measurements of CO2, CO and O3 were performed using a non-dispersive infrared analyzer (NDIR), an NDIR with gas filter correlation and an ultraviolet absorption analyzer, respectively. The continuous CO2 data processed for public use are available at our webpage (http://soop.jp/). Flask samples were also collected for laboratory analysis of CO2, CO, CH4, N2O, SF6, H2, O2/N2 and CO2 isotopologues (13CO2, 12C18O16O). A detailed description of the atmospheric observation system is provided elsewhere13.

In September 2009, continuous measurements of atmospheric CO2 and CH4 were commenced on VOS ships along both of the Southeast Asian routes using wavelength-scanned cavity ring-down spectroscopy (WS-CRDS) instruments (Picarro Inc., Santa Clara, CA, USA, models EnviroSense 3000i and G1301). Air sample was collected from the air intake set at the top deck of the ship (approximately 50 m above sea level) using a diaphragm pump placed in the observation room. The sampled air was dried before analysis to minimize biases due to dilution and pressure-broadening effects of water vapor on the WS-CRDS measurements. The sampled air was dehumidified by passing it through a sample-drying unit consisting of an electric cooler kept at +1°C and a Nafion Perma Pure dryer (Perma Pure LLC, Toms River, NJ, USA). The design and performance of the unit was very similar to that used for the CO measurements13. The sampled air was dried to less than ~0.3% water (as measured by the WS-CRDS) before the mole fractions of CO2 and CH4 were determined based on the water vapor content according to an instrument-specific water vapor collection function33. To our experience, the air samples were rarely contaminated with the ship's exhaust, when the ship sails at approximately 20 knot because the ship's exhaust is located at stern side. When the air samples were contaminated with the exhaust gas, we judged it by the CO2 and O3 measurements (CO2 increase and O3 decrease) and then rejected the data before the analysis.

For instrument calibration, we prepared a set of three natural or purified air-balanced standard gases with CO2 and CH4 (ca. 380, 400 and 420 ppm for CO2 and 1800, 2000 and 2200 ppb for CH4) in our laboratory to prevent pressure-broadening effects due to the different air compositions of the samples and standard gases. The standard gases were introduced into the WS-CRDS instrument daily in series, 10 min for each gas. The mole fractions of CO2 and CH4 in the standard gases were calibrated against NIES standard gas scales (NIES 09 for CO2, NIES 96 for CH4), which are traceable to World Meteorological Organization standard gas scales. The analytical precision for 1-min measurements by the WS-CRDS instruments of CO2 and CH4 were typically 0.05 ppm and 0.5 ppb (1 sigma), respectively. In this study, we used only the 1-min temporal mean CH4 and CO2 data from the continuous measurements because of the coarse resolution of the CO data, available only as 1-hour means from the gas filter correlation measurements and because the O3 data provide little information about CH4 emission sources.