Their study concluded that IPCC and similar standard landfill methane generation-based models overestimated emissions by factors ranging from 4 to It is not appropriate, however, to extrapolate these site-specific multipliers to other sites or to the national total due to site-specific differences in operational practices and climate.
As previously discussed in Chapter 2 , there are fundamental problems with the current inventory methodology, including a lack of systematic field validation for emissions as well as a lack of systematic correspondence with independent field measurements. The model provides improved site-specific estimates as the sum of cover-specific methane emissions with and without oxidation for min time steps and 2. Department of Agriculture models for average year weather data with 0.
Moreover, CALMIM has utility for other research and engineering applications when used with local annual weather data to examine annual emission trends or assess emissions from proposed alternative cover materials, or when paired with climate projections e. This JAVA tool directly models diffusive emissions inclusive of soil oxidation, without any linkage to a theoretical methane generation model. International field validation for CALMIM included a direct comparison of results to independent field measurements derived from a wide variety of techniques for 40 cover materials at 29 landfill sites on 6 continents e.
Bogner et al. Overall, model comparisons to field measurements resulted in a d index of 0. At specific sites, this model can provide a temporal framework for expected emissions with and without oxidation for comparison to field measurements. Figure 3. Here the model provides a temporal framework for expected emissions using year average weather data with and without oxidation for comparison to the field values.
This is directly attributable to an operational practice that is not universally practiced at U. Hence, new waste directly overlaid old methanogenic waste without an intervening cover, resulting in high emissions. At sites where this is standard practice, the total site emissions can be largely dependent on the daily filling area, and boundary conditions within the CALMIM model e.
For many other sites, however, a high percentage of total site emissions can be attributed to intermediate cover areas, which typically cover large areas and have thinner cover soils than final covers see Spokas et al. In general, estimation of landfill emissions using this model requires limited inputs: cover areas, their physical properties and thickness, and the extent of installed biogas recovery on a percent cover area basis.
CALMIM has also been applied in California to a new state-level inventory and compared with the existing state inventory using IPCC which assumed 75 percent biogas collection efficiency and 10 percent oxidation; Spokas et al. The three most common California cover types used for this simulation were taken from an independent dataset developed by the California Department of Resources Recycling and Recovery Walker et al.
Site-specific emissions between the two inventories varied inconsistently in both directions due to the different drivers for emissions, namely, mass of waste using IPCC and, more realistically, the combination of soils and seasonal climate using CALMIM see Figure 4. Use of and further improvements to field-validated, process-based models e. In particular, robustly linking cover-specific oxidation to site-specific climate is warranted.
Methane in coal can be generated thermogenically, as part of the coalification process, 6 or biogenically owing to activity of microbes. Because of these two different mechanisms of generation, typically coals of low coalification levels are targets for biogenic methane, whereas coals of high coalification level may contain thermogenic methane e.
Coal extracted by underground techniques is expected to have more methane because of its better preservation at greater depth. In addition, deeper coals typically have a higher coalification level because of deeper burial and also because their emissions are supplemented by the methane from affected underlying or overlying sediments. In contrast, shallow coal uncovered in surface mines has less methane primarily because of its easy migration through the shallow cover to the surface over geological time. However, methane content in coal can vary significantly between coal basins, between coal beds within the basins, or even within individual coal beds e.
In underground coal mining, activity data such as numbers of mines and quantities of coal produced are well known and emission estimates rely on stack-based sampling of. These numbers are regularly reported to the EPA. Ventilation systems in active underground mines are the largest source of coal-mining methane emissions. Gas emitted from walls and pillars enters the ventilation system of mines when it is not captured by boreholes.
Methane emissions from ventilation systems are assessed based on the airflow and the methane concentration in the ventilation air. While ventilation flow measurements do not show large variations being a function of the size and capacity of the specific ventilation fan , the methane content in the ventilation air can vary significantly in response to changes in coal-seam properties and to changes in daily coal production.
If emission levels are greater than 3. MSHA maintains a database of measured methane emissions from all mines with detectable levels of methane in the ventilation air MSHA, Independent of MSHA measurements, individual coal mines perform frequent methane emission measurements to ensure safe working conditions. Emissions are measured, typically weekly, with handheld methane detectors and flowmeters at the base of the ventilation air shaft in the mine.
Continuous monitoring devices are used primarily to warn miners if methane concentrations exceed 1. Consequently, gassy underground U. To prevent the occurrence of high methane levels in gassier mines, degasification of coal seams takes place prior to mining. This methane drainage reduces the gas content of the coal and decreases the risk of gas outbursts by decreasing the pressure in the rock formations Karacan et al. Degasification is accomplished by in-mine horizontal boreholes or surface boreholes, and can be carried out before or after mining Figure 3.
In addition, post-mining wells recover methane from the overburden. While horizontal boreholes are often used to capture methane, surface boreholes are used to control seam gas and are typically vented into the atmosphere. The amount emitted is monitored daily or every few days at the wellhead with methane detectors and flowmeters.
Some mines use continuous systems that monitor various parameters simultaneously. In addition to emission assessment based on ventilation and degasification described above and used in the GHGI, various empirical models have been developed for. Empirical methods typically require only a few input parameters e. To generate more accurate emission predictions from longwall mines, a modular software suite, Methane Control and Prediction, 7 was developed using artificial neural networks in combination with statistical and mathematical techniques Dougherty and Karacan, This software predicts emissions based on a number of parameters related to coal characteristics, mining conditions, and productivity, and can also conduct sensitivity analysis Karacan et al.
Even though current methane emissions from abandoned underground mines account only for 9 percent of the total coal mine emissions, the increasing number of. Gassy underground mines continue emitting methane after they are closed. The emissions are typically reduced compared to their active phase, but can still be substantial if gas can find conduits to migrate to the surface. The level of emissions varies depending upon many factors including gas content of the coal, mine flooding, the presence of conduits, the quality of mine seals, and the time since the mine closure.
In general, emission estimates from abandoned underground mines are based on the emissions during the active phase of the mine, assuming that emissions experience a hyperbolic decline after abandonment. The main challenge with the estimation of methane emissions from the abandoned underground mines is generation of an accurate decline curve. Methane adsorption isotherms, coal permeability, and pressure at mine abandonment are required to establish a reliable decline curve. Mine-specific data are used to fit the decline curve equations Karacan et al.
Estimates from abandoned underground mines carry uncertainties related to a decline-curve generation. With an increasing number of underground mine closings, this is an important category that requires improvement in methane emission predictions. Surface coal mines release methane as overburden is removed and the coal is exposed.
Methane can be emitted both from coal and associated clastic sediments overlying or underlying rocks that are affected by mining activities. Compared to underground mines, the level of emissions from the surface mines is much lower, primarily owing to low gas content of shallow coals that are mined from the surface. Therefore, emission measurements are not required for surface mines, and mine-specific emission data are rarely available. Consequently, the emissions are estimated using production data and coal and gas data.
As discussed in Chapter 2 , the emission factor currently used in the United States is based on percent of the in situ gas content of the coal EPA, b. Efforts to develop direct methane measurements or mine-specific assessments in surface coal mines of the United States and elsewhere have also been attempted. For example, open-path Fourier-transform infrared spectroscopy followed by. Gaussian-based plume dispersion modeling showed that methane emission rates can range over an order of magnitude for a single mine EPA, Use of a chamber method combined with measurement of surface gas emission flux in Australian surface mines yielded promising results but proved to be impractical to use.
That technique required good measurement coverage to obtain a representative methane value for the mine, which, because of safety issues and lack of access to some areas, was practically impossible Saghafi et al. Saghafi proposed a new Tier 3 method to estimate emissions from Australian surface mines based on an emission model that considered coal seams and surrounding horizons as individual gas reservoir units. The main input data required are in situ gas content, gas composition, and thickness of the gas-bearing horizons. In this method, two or three core drillings per mine are typically required to characterize a gas-emitting zone and to provide the main input of the model.
Because of the limitations of the standard gas content measuring method, different limits of gas content measurability can lead to significant differences in the estimation of methane emissions. These direct or mine-specific measurement efforts demonstrate that, although it is very desirable to estimate methane emissions for each surface mine, it is a highly challenging task because of variations in gas content and also because of the difficulty of gaining enough access to the sites to guarantee statistically sound measurement coverage.
Estimates of surface mine emissions are based on coal production data, often imprecise gas content, and assumed gas emission factors. Therefore, the estimates carry larger uncertainties than those from underground mines. However, underground mining has a much higher contribution to total methane emissions and therefore is a priority for efforts to improve methane emission estimates from coal mining.
Top-down emission estimates for methane, for the United States or any other region, rely on atmospheric measurements of methane and a quantitative understanding of the sources and sinks of methane in the atmosphere. Because U. Global methane monitoring networks provide this information. Monitoring of atmospheric methane was built on carbon dioxide monitoring efforts. It was initiated by the Rowland-Blake Group at the University of California UC , Irvine, in and is ongoing, making these observations the longest-running time series of atmospheric methane concentrations.
Air samples at approximately 45 sites distributed throughout the Pacific Basin from Alaska to New Zealand are collected four times per year, with many of the sampling sites located on remote islands in the Pacific and along the West Coast of the United States. Analysis of air samples also includes about halocarbons and hydrocarbons, including some that are potentially useful for understanding sources of methane. The sampling frequency is about once per week, although at NOAA observatories, continuous observations are possible because air is sampled and analyzed in situ.
Air samples have been collected from a variety of platforms including cargo ships, small aircraft, and tall telecommunications towers. The AGAGE network also measures more than 50 other atmospheric compounds, many of which are related to methane. There is a wide range of signals to be detected in atmospheric methane using spatially distributed observations that are sustained over time. The latitudinal gradient of methane the annual mean difference between the North and South Poles is about ppb, compared to the global average dry-air mole fraction the ratio of moles of methane compared to number of moles of all components concentration.
Much smaller longitudinal gradients occur between the Pacific and Atlantic: ppb, based on estimates of U. Differences in the Pacific-Atlantic gradient are particularly important for determining U. Near strong local sources, gradients can be much larger, hundreds to thousands of parts per billion over relatively small distances.
However, precision and accuracy are worse than what can be achieved using the ground-based network, making the quantification of large-scale trends more difficult. Given the impact of stratospheric methane and topography on column-averaged methane abundances—which are measured by the satellite—full atmospheric inversions are required to infer total U. On the other hand, satellite retrievals are useful for detection of localized high emissions at small scales over which variations in topography, stratospheric methane, and tropopause height are not significant.
Enhancements near strong localized sources can range from tens of parts per billion to greater than ppb Chen et al. For example, methane enhancements of 40 ppb were observed over the Four Corners region of the southwestern United States Kort et al. Uncertainties in top-down emission estimates are influenced both by uncertainties in atmospheric methane measurements and by uncertainties in the models used to estimate emissions based on atmospheric measurements Box 3.
The uncertainty of network observations plays an important role in determining the sensitivity of the network to spatial and temporal variability of methane, and this translates into information about source distributions and their variability. Using the current understanding of methane sources and sinks of methane, each 1-ppb increase of methane distributed globally requires emissions of 2.
The uncertainty in mean modeled global tropospheric methane abundance 1. On the other hand, space-based methane retrievals, which have a precision of ppb, are difficult to use for accurately quantifying gradients between the Pacific and Atlantic and defining methane upwind and downwind of the United States. The high-precision methane observation networks currently in place provide a rigorous mechanism for tracking changes in global emissions. For example, Environment and Climate Change Canada collects long-term observations at Alert, Nunavut, and other locations throughout Canada.
The use of monitoring observations from multiple institutions can significantly improve data coverage; however, it is necessary to ensure that the data are of comparable quality and are calibrated to the same scale. To facilitate this, the World Meteorological Organization has set up a framework to ensure that data submitted to the World Data Centre for Greenhouse Gases WDCGG meet quality and calibration standards and that relevant metadata are distributed along with the data.
The commitment to regularly submit data to the WDCGG varies, with some institutions providing frequent updates and others lagging by years. A map showing current sites for which greenhouse gas observations have been submitted is shown in Figure 3. Monitoring observations have also been made from commercial aircraft by European, Japanese, and Australian investigators Brenninkmeijer et al. These data provide information about the upper troposphere and lower stratosphere. Profiles are made during ascents and descents mostly in polluted urban environments, potentially making them useful for estimating urban emissions.
Global monitoring observations are also available for atmospheric trace species that are important for understanding the methane budget. This methane isotope is useful for attributing emissions to biogenic. Methyl chloroform can be used to infer the strength of the chemical sink of atmospheric methane because its emissions are thought to be well known, and its only chemical loss, as for methane, is by reaction with hydroxyl radicals e. Observations of some hydrocarbons such as ethane and propane may also help to quantify emissions from petroleum and natural gas production since they are co-emitted with methane from this source.
The ratio of the emissions of these higher hydrocarbons relative to methane varies considerably and is not well characterized at present Allen et al. However, both ethane and propane are increasing rapidly in the atmosphere, possibly due to increased petroleum and gas production in the United States Franco et al. Observations of methane column abundance from satellite platforms may significantly increase the spatial and temporal coverage of observational constraints Jacob et al.
Satellite measurements typically use methane absorption features in the shortwave or thermal infrared spectral range to derive methane abundances. Remote sensing in the shortwave infrared is based on absorption spectroscopy using the sun as a light source, which makes it very sensitive to methane in the entire atmospheric column, including near-surface variations e. However, using the sun as a light source excludes nighttime measurements.
Retrievals in the thermal infrared use blackbody radiation from the surface and the atmosphere as the light source and are less sensitive to the surface, with peak sensitivity in the free troposphere e. One of the primary advantages of remote sensing is that, in principle, it enables global, frequent coverage with a single instrument. However, satellite-based retrievals will never be able to be as accurate or precise as ground-based in situ instruments, as the measurements are always affected by other confounding factors such as aerosols, which can never be fully eliminated with passive remote sensing see Chapter 4.
Active measurements are unlikely to achieve true global coverage, however. Satellite observations of near-surface methane were spearheaded by the European and Japanese space communities. The first U. These higher-resolution instruments may allow for mass balance approaches to be used to estimate emissions with higher spatial resolution than is currently possible. Potential future missions might also be able to map localized plumes from space Thompson et al. The company has closely collaborated with numerous government and academic organizations. Data from the efforts are being made broadly available and several papers leveraging associated measurements have been or are in the process of being published e.
However, most of the NOAA tower network is not equipped to measure methane continuously, although there are some Earth Networks data available for urban areas, as noted above. Analysis systems deployed at tall-tower monitoring sites are based on commercial nondispersive infrared absorption sensors.
Measurements of methane are not possible using these analyzers. An example is Walnut Grove, California, where a cavity ring-down spectrometer was installed in September Air samples have also been collected since the late s using light aircraft Sweeney et al. Currently, profiles of GHGs including methane are collected every 2 or 3 weeks at 17 locations Figure 3. This aircraft program has provided information about large-scale horizontal and vertical methane gradients across the United States and their seasonal cycles. A climatology constructed from aircraft profiles clearly shows the accumulation of methane from west to east across the continental United States Sweeney et al.
Regional atmospheric methane observations can be made using networks of tower sites similar to those used in continental networks, but at a smaller scale. The goal is to provide top-down atmospheric constraints on California methane emissions for comparison with bottom-up inventories Figure 3. Regional tower and tall-building-based observations have also been made at the scale of individual cities, including Indianapolis Lamb et al. Short-term, regional observations have also been conducted by aircraft, often in regions dominated by particular types of methane emission sources.
Table 3. In general, these studies involve making measurements upwind and downwind of the source region during periods when the atmosphere is assumed to be well mixed. The instrumentation used in aircraft measurements can be varied, but generally relies on instruments with high time resolution and precision.
Measurement & Monitoring Systems
Aircraft measurements are used to estimate regional emissions by multiplying 1 the methane concentration difference between upwind and downwind measurements by 2 the ventilation rate for the region. These types of observations have revealed significant regional variability in the intensity of methane emissions units of methane emissions per unit methane produced from petroleum and gas supply chain sources.
A variety of models are used to deduce information about emission and sink processes from spatially distributed time-series measurements of methane. At global, continental, and regional scales, forward and inverse modeling methods are used. At regional scales, some analyses employ mass balance approaches. Both forward and inverse modeling approaches are powerful analysis tools that can increase understanding of the global and regional budget of methane.
Inverse techniques are diagnostic because they allow a look backward in time to understand trends in emissions. They may provide guidance on whether policy aimed at mitigating methane emissions is effective, and they may alert scientists of increased emissions from Arctic wetlands and permafrost. Diagnostic modeling also can lead to improvements in prognostic modeling. By using inverse modeling to evaluate bottom-up models of emissions, improvements can be made to the bottom-up models, and the result may be better confidence in coupled climate—carbon cycle predictions.
The forward approach involves use of bottom-up estimates of emissions and sinks, along with an atmospheric transport model to simulate atmospheric methane that can be compared with observations. Bottom-up estimates come from inventories of the type described in Chapter 2. Process model data or up-scaled flux observations can be used to prescribe emissions from natural wetlands, as well as methane uptake in dry soils when soil acts as a sink.
Differences between forward simula-. The canonical example of this type of approach applied to atmospheric methane is the study by Fung et al. The imbalance between sources and sinks leads to increasing atmospheric methane, in agreement with the observed methane growth at that time. Fung et al. Interestingly, they also found that several budget scenarios could satisfy observational constraints, a finding that has important implications for the inverse approach. While the forward approach uses models of atmospheric transport to convert emissions to atmospheric abundance, the inverse approach converts atmospheric abundance to emissions.
Differences between atmospheric methane simulated using atmospheric transport models, first-guess emissions i. The numerical techniques employed range from simple mass balance approaches to data assimilation methods similar to those used in numerical weather forecasting. A very simple global inverse model approach using the global continuity equation for atmospheric methane was used by Dlugokencky et al. Spatially resolved atmospheric transport models range from one-dimensional diffusion models Bolin and Keeling, , to zonal average models e.
Most inverse models are based on Bayesian inference, wherein new information coming from observations is systematically combined with bottom-up information the priors Hein et al. The resulting estimated emissions posterior estimates are in optimal agreement with both the priors and the observations, given. In a Bayesian analysis procedure, the solution is strongly influenced by the relative weighting of information coming from the prior estimates and information coming from observations.
This weighting is defined by estimated uncertainties of the prior emissions and observations. The observation uncertainty represents transport model uncertainty as well as measurement uncertainty; however, the latter is typically much smaller than transport model uncertainty for in situ observations. Defining the prior uncertainty estimates is challenging and may not be well constrained by independent information.
For example, bottom-up estimates of emissions from inventories often lack associated uncertainty. In addition, it is especially challenging to quantify model transport error, especially for measurement sites where the distribution of local sources may not be adequately known; therefore, it is difficult to determine whether model-data differences are due to a lack of knowledge of sources or transport errors. For regional inverse modeling, McKain et al. The atmospheric concentration data alone do not constrain both. Simulation methods for atmospheric transport are developmental, and current uncertainties limit the reliability of regional inversions.
Inverse models have proved to be an important tool, yet challenges to their usage remain, notably 1 the inability of the surface network to adequately reflect the full spatial variability of emissions, leading to multiple solutions that may significantly differ in spatial allocation of emissions and allocation of emissions among sources; and 2 the requirement that point measurements be accurately simulated with typically coarse-resolution transport models. Inverse modeling could provide information about variability and trends in atmospheric methane.
It could also provide important insights into performance of bottom-up emission models, some of which may be coupled with climate models that are capable of predicting feedbacks between trace gas emissions and climate, leading to improved confidence in climate predictions. For example, by seeing how prior estimates of wetland methane emissions are changed by use of observations, biases in wetland emission process models may be identified and remedied. Understanding global methane emissions is important for understanding U. S sources. Houweling et al.
Maintenance, Measuring & Monitoring Software
Possible reasons for this include overestimated bottom-up emissions at high northern latitudes and underestimated bottom-up emissions at low and southern latitudes, as well as biases in model transport and chemical loss. In addition, it is difficult to rule out biases in atmospheric transport models. For example, a too-stable planetary boundary layer could systematically lead to underestimated emissions.
Adequate observational coverage in space and time is required to fully constrain inverse models at national or regional scales. There are only about surface sites globally that measure atmospheric methane, and many of these sites are sampled only weekly, although a small number of sites collect continuous measurements. Many surface network sites have been selected to represent the background atmosphere remote from strong local sources.
Some regions are therefore inadequately resolved by inverse models, for example, the tropics, where long-term monitoring is often logistically challenging and emissions are likely to be significant. Sparse data coverage has important implications for inversions; Hein and Heimann and Hein et al. Improving atmospheric transport models will also lead to more accurate estimates of emissions using inverse models.
Patra et al. Analysis indicated a considerable range in interhemispheric transport exchange time among models 1. By using multiple inverse models, Locatelli et al. At continental scales, transport model differences led to even larger differences, with differences for North America of up to 36 Tg methane yr —1. Coupling increased observations of atmospheric methane with improved measurements of important diagnostic quantities for atmospheric modeling such as planetary boundary-layer depth will likely help improve the accuracy of emission estimates, increasing observational constraints and providing critical datasets that may be used to improve atmospheric transport models.
Observations of column-average methane from satellite platforms may significantly increase the spatial and temporal coverage of observational constraints. Satellite data can be used in atmospheric inversions in the same way as ground-based observations. In fact, both data streams can be used in a single inversion, helping to ensure consistency and to point out potential biases in satellite data, which typically have lower accuracy due to their indirect measurement technique.
Current satellite instruments have been shown to have persistent biases in space and time e. Remotely sensed observations of column methane using ground-based upward-looking Fourier spectrometry e. In terms of monitoring, successive instruments will be needed to monitor over multiple decades, and care will have to be taken to ensure that instruments are comparable. Multiple studies have successfully used satellite data in atmospheric inversions e.
Bergamaschi et al. They also proposed that use of the satellite retrievals and estimation of emissions at grid scale resulted in information about how emissions vary at subcontinental scales, a result that was later questioned by Bousquet et al. The use of their water-vapor-constrained bias correction led Houweling et al. Some attempts to increase transport model resolution have been made using global models with increased horizontal resolution over regions of interest, a computationally cheaper alternative to global high resolution.
In particular, Bergamaschi et al. Turner et al. It is difficult to accurately simulate point measurements with a coarse-resolution model because of variability due to strong local sources, surface topography, and high-frequency atmospheric variability. For this reason, some recent studies have used regional models that simulate atmospheric transport at resolutions of 10 km or less.
A surface sensitivity or response function matrix is constructed, and inverse techniques are used to estimate sources, just as for the global case. Driving meteorology from high-resolution global analyses or regional models such as the WRF Weather Research and Forecasting; Skamarock and Klemp, model is used to calculate particle trajectories. The advantage of using a regional atmospheric model is that transport can be simulated at higher resolution and is likely to be more accurate than with a coarser global model. The disadvantage is that boundary conditions at the edge of the domain must be specified, and there is not good observational coverage to do this accurately.
The solution is very dependent on the estimated boundary condition and Gourdji et al.
Enersize Q+ online measurement & monitoring – Enersize
In addition, Miller et al. They found that the inverse estimates were substantially higher than those from bottom-up inventories and suggested that natural emissions from wetlands could be higher than bottom-up estimates. No systematic differences between global nested-grid and Lagrangian inversions were highlighted; however, the Lagrangian inversions used background methane from the global inversions, thereby eliminating a major source of difference between the two approaches.
The study by Bergamaschi et al. Inverse modeling has also been used to estimate emissions at urban and petroleum and natural gas basin scales. For example, Lamb et al. They found significant differences between their inverse estimates and both bottom-up and mass balance estimates using measurements taken from aircraft. They found that 48 percent of methane emissions were from biogenic sources, with the remaining portion from fossil fuels, including citywide diffuse leakage. Jeong et al.
The authors conclude that landfills dominate the total emissions, and methane emissions from natural gas systems are approximately 0. Barkley et al. They found agreement between their estimates and mass balance estimates based on aircraft observations, as well as leakage rates. Estimated emissions for the contiguous United States show a large spread, about Tg methane yr —1 for Figure 3. These results are described by Saunois et al. These differences account for the range in emission estimates. Inversions that sequentially estimate emissions over time time-dependent inversions are shown as lines, while inversions representing time averages are shown as points.
Total methane emissions are shown because estimates of total emissions are more robust while source attribution is less certain due to sparse observations and inaccurate prior estimates. Inversions using space-based retrievals of column average do not appear to significantly differ from those using only in situ observations, at least for the continental United States. The inversions by Wecht et al. S emissions, and both studies used a global modeling framework while solving for U.
S emissions at high spatial resolution km and 50 km. The inversion by Miller et al. Top-down estimates have also been produced for specific regions of the United States. The studies by Miller et al. Regional top-down estimates showed that emissions from fossil fuel production in the Four Corners region of the southwest are likely to be much higher than the EDGARv4.
More recently, Smith et al. The top-down approach has also been used to estimate emissions from the state of California. Wecht et al. Likewise, the study by Zhao et al.
Personalize your experience by selecting your country:
In contrast to forward and reverse inversion methods, the models used to estimate emissions for regional aircraft observations are much simpler. Aircraft transects are flown upwind and downwind of the study region. The total flux of air pollutants into and out of the region can be calculated by multiplying average concentrations of the air pollutant along the transect by wind speed perpendicular to the transect, the length of the transect, and the mixing height.
Regional emissions are then estimated by subtracting the flux out of the region from the flux into the region.
IN ADDITION TO READING ONLINE, THIS TITLE IS AVAILABLE IN THESE FORMATS:
There are several assumptions associated with this approach to estimating emissions. Since flights are typically done over multiple hours and upwind and downwind data collection can be done hours apart, the emission estimates assume that wind speeds, wind directions, and mixing heights remain constant over the sampling period. Since most aircraft flights sample at a single elevation or do only limited vertical spirals, the method also assumes that air pollutants including methane emissions are uniformly distributed throughout the mixed layer.
These and other assumptions, such as limited sinks in the region, typically lead to uncertainty estimates for individual aircraft flights in the percent range see, e. In addition, uncertainties were reduced by doubling the number of transects and employing a wind profiler. For the reasons mentioned previously, accurate ground-based and spatially contiguous satellite observations should be seen as complementary to other approaches, with the combined systems overcoming weaknesses in the individual elements. The tradeoffs between frequent global coverage and lower accuracy and precision need to be taken into account when evaluating advantages and disadvantages of the accurate long-term ground-based network versus satellite data records.
Accurate hemispheric averages and decadal variations can only be fully captured by long-term in situ sampling networks, which must be the backbone of any global observing strategy. On the other hand, space-based observation offers the unique capability to spatially map local gradients of atmospheric methane across the globe, revealing source processes and, potentially, emission rates. With improving spatial resolution and temporal coverage, satellite data may also help identify key regions where the largest discrepancies between observed and expected based on inventories methane abundances exist.
The Four Corners region in the United States is a primary example of how a regional hotspot was detected from space; the ensuing follow-up field investigations used both in situ samples and airborne remote sensing e. This can in turn improve inventory development on larger scales by identifying the drivers of underlying discrepancies per source category. This multiscale strategy could involve scales ranging from direct flux measurements at the facility level to the global view from space. High-quality, long-term, multiscale surface and space-based data records are necessary for quantifying and tracking changes in methane emissions on regional scales.
Top-down and bottom-up approaches yield complementary information about methane emissions. Bottom-up methods provide information about the magnitudes and patterns of emissions from specific sources, and as a result they provide the type of information that is necessary to mitigate emissions.
As shown in Chapter 2 , however, bottom-up inventories of emissions may not account for all sources, and, as outlined in this chapter, the methods may have uncertain or inaccurate activity data and emission factors. Top-down estimates of emissions include emissions from all sources even unknown ones because they are based on atmospheric observations, but they may have difficulty in attributing emissions to specific sources and be subject to errors in atmospheric transport as discussed previously. Top-down approaches may also need to use prior emission estimates to constrain solutions due to sparse data coverage, and these may also have biases and errors that lead to biased top-down emission estimates.
Both top-down and bottom-up measurements used to estimate emissions can also be spatially and temporally sparse, leading to biases. For example, when aircraft measurements are used to obtain data, the flights are typically limited to just a few days, and the measurements are generally done in midday when the atmosphere is well mixed. These measurements will therefore lead to information about emission sources that is limited to midday hours, and these emissions may be different than at other times of day, which limits direct comparisons with methane inventories such.
The complementary information provided by top-down and bottom-up methods offers the opportunity to combine their strengths in coordinated measurement campaigns. Only a limited number of highly coordinated campaigns have been performed that utilize both types of methodological approaches, however, and the most comprehensive of these studies have been performed in regions dominated by petroleum and natural gas supply chain emissions.
In , the Environmental Defense Fund organized a coordinated top-down and bottom-up measurement campaign in the Barnett Shale petroleum and gas production region in north central Texas. Multiple teams of investigators performed aircraft measurements of methane and ethane, leading to top-down emission measurements on multiple days. Several teams of investigators did ground surveys downwind of individual sites, collecting site-specific emission estimates for hundreds of petroleum and natural gas sites, including production sites as well as other, downstream sources.
All of this bottom-up and top-down information was synthesized into a portrait of. In the Fayetteville Shale natural gas production region in Arkansas, a top-down and bottom-up comparison was performed in with DOE and industry sponsorship Bell et al. The study featured contemporaneous application of top-down and bottom-up techniques with site access and operational data from the major natural gas producers in the region to develop a spatiotemporally resolved bottom-up inventory. This effort found significant day-to-day variability in bottom-up emission estimates, depending on the nature of the planned natural gas production operations occurring on particular days.
Because operations at individual sites varied from day to day, there was significant spatial variability in emissions from day to day.
Top-down measurements confirmed the daily variability in emissions, and overall, the study confirmed that knowledge of both local facilities and daily operational schedules are important in comparing top-down and bottom-up emission estimates. Coordinated, contemporaneous top-down and bottom-up measurement campaigns, conducted in a variety of source regions for anthropogenic methane emissions, are crucial for identifying knowledge gaps and prioritizing emission inventory improvements. Careful evaluation of such data for use in national methane inventories is necessary to ensure representativeness of annual average assessments.
Understanding, quantifying, and tracking atmospheric methane and emissions is essential for addressing concerns and informing decisions that affect the climate, economy, and human health and safety. Atmospheric methane is a potent greenhouse gas GHG that contributes to global warming.
While carbon dioxide is by far the dominant cause of the rise in global average temperatures, methane also plays a significant role because it absorbs more energy per unit mass than carbon dioxide does, giving it a disproportionately large effect on global radiative forcing. In addition to contributing to climate change, methane also affects human health as a precursor to ozone pollution in the lower atmosphere. Improving Characterization of Anthropogenic Methane Emissions in the United States summarizes the current state of understanding of methane emissions sources and the measurement approaches and evaluates opportunities for methodological and inventory development improvements.
This report will inform future research agendas of various U. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website. Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.
Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text. To search the entire text of this book, type in your search term here and press Enter. Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available. Do you enjoy reading reports from the Academies online for free? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.
- Math games and activities from around the world.
- Knowledge Management in the Intelligence Enterprise!
- Europeanization and Transnational States: Comparing Nordic Central Governments (Routledge Advances in European Politics).
Get This Book. Visit NAP. Looking for other ways to read this? No thanks. Page 78 Share Cite. Page 79 Share Cite. Technique Method Advantages Disadvantages Point-source measurements Measurement of emissions from fixed points based on flow rate and methane composition. Measures total methane emissions from individual point sources e. Captures temporal trends if deployed for extended time periods. Limited number of methane sources are emitted as point sources. Labor intensive to quantify spatial and temporal variability requires a large number of individual measurements to capture variability.
Enclosure chamber techniques Direct measure of emissions from small area or number of animals. Quantifies diffusive emission rates from a small source area typically 1 m 2 or less during daytime or nighttime conditions.
banriburesna.ga Accurately measures emissions from individual or small groups of animals in a controlled environment. Does not rely on atmospheric modeling to derive fluxes. Quantifies rates for soil oxidation of atmospheric methane i. Labor intensive to measure the variability of emissions over large source areas requires geostatistical techniques, a large number of chamber measurements, and ancillary information.
Provides an instantaneous measurement that must be repeated to capture temporal trends. Single enclosures may not capture all variability in emissions. Page 80 Share Cite. Technique Method Advantages Disadvantages Micrometeorological techniques Tower-based vertical measurements of gas concentrations and atmospheric parameters with standard modeling approaches to calculate fluxes.
Examples include flux gradient, integrated horizontal flux, eddy covariance, etc. Measures continuously over time to capture temporal trends in emissions. Measures uptake of atmospheric methane i. Appropriate topographic and meteorological conditions are necessary for technique to work properly.
Nighttime measurements are a challenge. Perimeter facility line measurements Measurement of path-integrated methane along boundaries of a source area i. Measures total methane emissions from variable-sized source areas. Allows long-term continuous monitoring to capture temporal trends in emissions. Difficult to isolate the different sources in source area depending on distribution and meteorological conditions. Difficult to determine the area contributing to flux.
Page 81 Share Cite. Measurement of methane and tracer concentrations across well-mixed downwind plumes to derive emission rate. Measures total methane emissions from source area. Measures complex sources or quantifies the uncertainty in the emission estimate multiple tracers. Difficult to isolate individual sources within source area depending on layout and meteorological conditions.
Appropriate meteorological conditions are necessary for technique to work properly. Vulnerable to bias if the locations of tracer release differ significantly from the location of methane release. Labor intensive to measure the spatial and temporal variability of emissions over many sources. Inverse dispersion modeling Measurement of downwind methane concentrations with estimated or measured meteorological parameters to estimate the flux rate from point and area sources. Estimates total methane emissions from point and area sources.
Estimates temporal trends when measurements are made continuously. Difficult to isolate various sources within the source area depending on source layout and meteorological conditions. Regional-scale methods are not fully developed. Accuracy may vary depending on the source to be measured.
Page 82 Share Cite. Technique Method Advantages Disadvantages Facility-scale in situ aircraft measurements Multiple vertical measurements of atmospheric methane and wind-speed gradients above a source area to derive an emission rate. Captures temporal trends with repeated overflights. Generally cannot isolate individual sources within source area unless useful source-specific tracers can be co-quantified.
Appropriate meteorological conditions sufficient vertical mixing of surface emissions at flyover elevations, typically midday conditions are necessary. Requires multiple flights to capture temporal trends in emissions. Generally limited to higher-emitting sources lower detection limits are much higher than point-source techniques.
Page 83 Share Cite. Other radiatively active gases and hydrocarbon gases are also measured. High precision Consistent measurements across multiple sites Long time series Limited spatial coverage. Towers Methane by infrared spectrometry at precise infrared wavelengths. Time-series measurements of concentrations, analyzed by eddy covariance or by inverse modeling. High precision Consistent measurements across multiple sites Long time series Sparse spatial coverage, potential small-sensitivity footprint.
Challenging to apply to individual facilities and distinguish confounding sources.
Aircraft mass balance measurements Measurements upwind and downwind of source region. High-time-resolution instruments. Once you have identified your measurement and monitoring requirements, you need to establish systems to collect and consolidate these measures. To do this, you need to define criteria to compare these measurements. Establish measurement devices or tools that would be required to take these measurements. Along with methods to collect and consolidation, establish methods to check that the results of measurements are valid. Analysis and Evaluation. Analysis is the process of investigating data to determine relationships and trends.
You may use various statistical tools like Pareto analysis, fish-bone analysis, 5-why analysis, etc. Evaluation is done to ensure adequacy, suitability and effectiveness of health and safety requirements. This activity is most often related to monitoring activities. Occupational health complaints, work environment monitoring and health surveillance of workers are some of the elements that need to be monitored in an organization. The results of analysis and evaluation shall be used to take actions to eliminate root causes which are the reason for negative feedback or measurement going beyond the targets established.
Analysis and Evaluation of data may specify a number of areas of concerns or risks. Adequate actions should be taken pro-actively to ensure these risks or problems are adequately addressed before they reach severe levels and are difficult to con.