英文摘要: | Methane (CH4) is one of the most important greenhouse gases, and an important energy carrier in biogas and natural gas. Its large-scale emission patterns have been unpredictable and the source and sink distributions are poorly constrained. Remote assessment of CH4 with high sensitivity at a m2 spatial resolution would allow detailed mapping of the near-ground distribution and anthropogenic sources in landscapes but has hitherto not been possible. Here we show that CH4 gradients can be imaged on the 2 scale at ambient levels (~1.8 ppm) and filmed using optimized infrared (IR) hyperspectral imaging. Our approach allows both spectroscopic confirmation and quantification for all pixels in an imaged scene simultaneously. It also has the ability to map fluxes for dynamic scenes. This approach to mapping boundary layer CH4 offers a unique potential way to improve knowledge about greenhouse gases in landscapes and a step towards resolving source–sink attribution and scaling issues.
Identifying sources and sinks of CH4 and comparing their relative magnitudes in landscapes is challenging but important. CH4 is the second most important greenhouse gas at a 100-year perspective1 and has a high value for society as an energy source. Atmospheric levels of CH4 have increased 2.5-fold since 1750 (ref. 1) but the reasons for this increase are not as clear as for carbon dioxide (CO2). For example, although atmospheric CO2 levels have increased steadily, the accumulation rate of CH4 has varied for unknown reasons2. Suggested explanations that are based on the balance of emissions from fossil fuels and wetlands2 are difficult to verify and alternative explanations cannot be excluded because sources and sinks are too poorly constrained. CH4 is produced by methanogenic archaea in anaerobic systems including sediments and water-saturated soils, gastrointestinal systems of animals, biogas production and waste management systems2, 3. CH4 is also released from natural gas handling and combustion processes2. The major sinks are believed to be atmospheric oxidation and microbial oxidation in soils, sediments, and water2, 3. Wetland plants, or bubbling through shallow inland waters, function as gas conduits from anaerobic sediments. Similarly, there are also hot spot sources in agriculture (for example, rice paddies, waste lagoons, and ruminants), and industrial and urban environments (combustion and gas distribution leaks). Many if not most large sources, both natural and anthropogenic, are confined to local sites with a patchy distribution across landscapes. The sinks may also be scattered in the landscape on the basis of, for example, local moisture levels in soils. Because of the difficulty in quantitatively assessing the spatial variability of sources and sinks our current knowledge is probably biased and incomplete. A fundamental limitation in our ability to identify and compare CH4 sources and sinks is related to the spatial scales of available measurement techniques. Bottom-up methods often rely on flux chamber or point concentration measurements. Flux chamber measurements have a well-defined but very small footprint (typically sub-m2) and cannot easily be used to cover larger areas. High- frequency measurements can be obtained by eddy covariance (EC) and gradient-based flux assessments with larger footprints at ha to km2 scales (ref. 4), but specific sources and sinks within the footprint cannot be resolved. EC and gradient flux footprints are based on statistical probability distributions, vary over time, and lack verifiable boundaries. A less common approach is the backward Lagrangian stochastic (bLs) technique5 which uses a laser and a reflector for each line of sight and can be used to locate a point source (or several sources depending on the number of lines of sight used) or estimate emission rates through dispersion model predictions. The dispersion models also have footprint uncertainties and specific infrastructure is required for each line of sight (such as the laser source and reflector), which limits the spatial distribution of the measurements. Several satellites have been or are now mapping CH4 on a global to regional scale, including SCIAMACHY (ref. 6), GOSAT (ref. 7), AIRS (ref. 8), IASI/AMSU (ref. 9), and the planned CarbonSat (ref. 10) and GRIPS (ref. 11), all having km-scale spatial resolutions. Satellites are very useful for their spatial coverage and have been successfully used in many projects for following regional patterns12, 13, 14, 15, but two drawbacks are the low spatial resolution and difficulties in resolving CH4 at the surface–atmosphere boundary layer where the source/sink patterns are revealed. A recent example is the four corners CH4 hotspot, a 6,500 km2 coal mining area in the US emitting enough CH4 to be seen from space but still measuring only a few pixels in SCIAMACHY images16. Although successfully mapping atmospheric CH4 content, the large pixel sizes limit our ability to link CH4 levels to environmental drivers that may differ between different types of environments/land use. Remote sensing of CH4 from aircraft is also in development. Examples are AirGRIPS (gas-filter correlation radiometer) and the MaMap spectrometer17, giving a resolution of 33 × 23 m at 1,000 m altitude. Higher-resolution (several m2) measurements of strong CH4 sources have been made in both the shortwave18, 19 and thermal IR (ref. 20) from high altitudes, representing important progress. However, a technique with the ability to map lower levels of near-ground CH4 in landscapes at very high spatial resolution (sub-m2), having a high enough spectral resolution to ensure separation of CH4 from other gases, yet with good spatial and temporal coverage, and the ability to measure flow velocities directly from high-speed imaging, would substantially increase our capacity to identify, resolve, and compare different sources and sinks. In turn, this would lead to new possibilities for understanding the variability of CH4 in the atmosphere, detecting and minimizing CH4 leakage or emissions from anthropogenic processes, and also validating how environmental change (for example, land-use and climate change) affect CH4 source attribution in climate models. We here present a new and generally applicable technique based on thermal IR hyperspectral imaging for landscapes that does not require a priori knowledge of source localization.
Hyperspectral imaging can be described as imaging that records a spectrum for each pixel simultaneously in a scene. Thereby a three-dimensional data cube is generated for each imaging sequence with two dimensions defining the scene spatially and a third dimension holding the spectral information (Supplementary Fig. 1). In the thermal IR part of the electromagnetic spectrum this is often a passive technique with patterns of absorption or emission lines, depending on whether the background is hotter or colder than the gas (the temperature contrast). Those patterns can be used as fingerprints of individual chemical compounds in the line of sight, making assessment of concentration gradients possible (Supplementary Fig. 2). Our hyperspectral camera, described in the Supplementary Methods, was developed for optimized detection of CH4 (7.7 μm band) allowing more sensitive quantification of concentration gradients than were previously avilable. Using all the spectra in a data cube and spectroscopic radiative transfer modelling at a high spectral resolution (0.25 or 1 cm−1) a time-averaged CH4 image can be calculated pixel-by-pixel (described in Supplementary Methods). The high imaging frequency of the camera during data acquisition (images including all spectral lines) also made it possible to separately derive air motion from H2O and CH4 motion and to construct air flow movies. In cases with high enough CH4 fluxes and low humidity the CH4 can be followed directly, while water vapour can be used as the air flow tracer in other cases. The presented technique can therefore generate sensitive static spectra by aggregating information over the image collection time (0.25–2 min per cube) to detect and measure the average amount of CH4 with high precision, as well as construct air flow movies. By combining the gradients in CH4 levels quantified from spectra with the information about net air movement, corresponding average CH4 fluxes during the image collection period can then be assessed. Thus not only concentration gradients but also fluxes can be calculated from the obtained images and spectra for both hot-spot and diffuse emission sources. After testing the system extensively in the lab, several successful field measurements were made. Below we present CH4 images of different environments to demonstrate the ability of the system to remotely map CH4 at ambient levels (~1.8 ppm; parts per million by volume) under field conditions (typically a few °C background-gas temperature contrast). We highlight examples of CH4 mapping, showing the ability to map concentration gradients, find emission hot-spots in the landscape, and quantify CH4 fluxes. A summary of the scenes and measured CH4 fluxes are given in Table 1, including a comparison with typical fluxes found in the literature where available. In situ measurements for comparisons were also made using an infrared Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) greenhouse gas analyser (Los Gatos Research, DLT 100 or UGGA).
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