Assessing Measurement System Performance Needs

Carbon management system performance metrics will likely include compatible quantitative and societal measures. Quantitative information on total emissions, for example, from national and sub- national geographic or political regions, cities, states, or provinces, provide a means to gauge the overall effectiveness of reduction efforts. Information at multiple temporal and spatial scales assist in identifying locations and tunes within a system where actions may be taken or modified to optimize management efforts and gauge progress of individual activities, as well as to assess the level of target attainment at particular times. The Paris Accords (COP21, 2015) of the UNFCCC established the Intended Nationally Determined Contribution mechanism where nations across the globe have pledged greenhouse gas reductions. At this writing, 164 nations har e documented their intent (INDC, 2019). Similar statements and legislation developed by sub-national governments illuminate local and regional needs. Nongovernmental and private sector communities har e focused on these issues and are becoming closely involved in carbon management-related activities, usually contained within sustainability and resiliency efforts.

Assessing quantification capability can benefit from some of these governmental statements. Reduction targets taken from selected INDCs and greenhouse gas reduction legislation of some U.S. states are shown in Table 1. Where carbon neutrality is a goal, the reduction target is taken as 100% relative to the baseline year. Measures of the degree of attainment are dependent upon baseline year quantification uncertainty and on understanding of measurement system performance. One- and five- year timeframes reflect measurement system performance needs consistent with yearly national reporting cycles, with the 5-year cycle of the stocktake concept defined within the Paris Agreement. Stocktaking is intended to be a means for analysis of national progress toward an INDC goal at 5-year intervals, beginning in 2023. It will give national governments the opportunity to assess implementation effort effectiveness and allow collective efforts to assess progress towards achieving the purposes of the Paris Agreement. At this writing, the mechanisms to be used for the global stocktake are under negotiation. Although procedures to be used are not agreed upon, carbon management system informational needs can be estimated fr om INDC information. Meeting such performance targets is challenging for some current measurement capabilities, but these targets provide useful goals for design and development of carbon measurement capabilities.

IPCC Inventory Reporting Methodologies

In cooperation with the UNFCCC, the Task Force on National Inventories of the International Panel on Climate Change (IPCC-TFI, 2019) has produced extensive guidance on national data acquisition,

Table 1. National or U.S. state greenhouse gas emission reduction targets.




Target year

Base year



5 Year reduction

United States[1]






European Union

> 40%






60%-65%/unit GDP



2.4%-2.6%/unit GDP

12%-13%/umt GDP

California[2] [3]
























New York[5]






Washington State[4]














compilation, and reporting methods. These are widely used by nations developing and making greenhouse gas emissions inventory reports and assist in producing reliable, accurate, consistent and comparable inventories of emissions and removals of greenhouse gases. The initial task force guideline documents were published in 1996 and subsequently revised, with the most recent revision having been published in 2006 (IPCC-TFI, 2006). A subsequent revision is underway, with release planned for 2019. TFI methods have relied upon a straightforward and widely-accepted methodological approach that combines information of the extent of a human activity, activity data, with a parameter that quantifies emissions or removals on a mass basis for that activity.

This is often expressed in simple mathematical form by the following:

This model is applied over many economic activities that emit or remove atmospheric greenhouse gases. Implementation can be straightforward using references, economic, and/or measurement data. Activity data can be taken from a range of sources derived from largely economic information concerning pertinent activities, e.g., fuel importation and consumption data or transportation data, generally from publicly available sources. Emission factor data can range from default values to very specific values based upon well-documented methods associated with specific process characteristics within a particular nation.

Emission factors are routinely expressed in terms of the mass of greenhouse gas emitted per unit activity, e.g., weight, volume, distance, or duration. In physical terms, these are a rate of flow to/from the

Table 2. Examples of emission, activity factor types and units.

Activity type

Activity factor

Emission factor

Emission mechanism

Power Generation

Terajoules (TJ)


Fossil Fuel Combustion

Natural Gas Distribution

Miles of pipeline

Grams of CO,M per mile of distribution piping

Methane Leakage

Vehicle Transportation

Vehicle Miles Traveled

Grams of СО,ед per mile traveled

On-Road Emissions'*

atmosphere per unit activity, e.g.. kilograms of CO, or CO, equivalent[7] [8] (CO,^) per activity rmit. Often the averages of available data of acceptable quality for an activity are used as emission factors, examples are given in Table 2. The IPCC-TFI methods only apply at national scales, although the underlying methodology is applicable sub-nationally, and the Paris Agreement recognized the need for efforts by sub-national governments and the private sector. To facilitate flexibility and applicability of this approach to emissions data compilation and reporting over a wide range of activities, a three-Tier approach is used, where Tier 1 uses default emission factors with Tiers 2 and 3 progressing to a greater specificity as determined by a nation's situation.

Quantification technologies and methods

Emissions and uptake quantification fall into two classes: The top-down and bottom-up classes mentioned above. Bottom-up approaches primarily use the emission/activity factor model. Top-down approaches are based on atmospheric observations of GHG mole fraction, using methods that range from physical sampling to remote sensing coupled in some way with determination of pertinent dynamics of the atmosphere generally or specifically within a specified region. There is a rich literature describing various aspects of both quantification methods. The references contained in this chapter are rich sources for further investigation and research, if so desired. The remainder of this chapter will summarize some of the more widely used, including brief descriptions of the processes where they are often applied.

4.1.1 The energy^ sector

Fossil fuel combustion for energy generation, whether for thermal or electrical power generation, and for transportation, comprises approximately 80% of the U.S. 2017 inventory (EPA, 2019). Equation (2), derived from equation (1), reflects the use of fossil fuel consumption as the main parameter describing energy production and utilization activity. Fuel consumption associated with several types of energy sector process activities are defined by the IPCC-TFI. Hie four main categories of energy sector emissions are: (1) primary energy source exploration and exploitation, (2) conversion into more useful energy sources, e.g., refining of crude petroleum to products such as fuels for vehicles or energy production, (3) fuel transmission and distribution, and (4) fossil fuel usage in stationary and mobile applications.

Economic statistics most often provide the source for fuel consumption data. It should be noted that fuel consumption has units of Terajoules (TJ), the amount of energy produced by a fuel's combustion. Use of fuel consumption as the activity parameter assumes that fuel carbon content is directly equated to the complete conversion of fuel carbon to COv i.e., frill oxidation. This necessitates the use of two additional fossil fuel parameters, heat energy or calorific content for a specific fuel and a unit’s conversion factors. Although combustion is usually the end result, the fossil fuel supply chain has recently been investigated for leakage to the atmosphere. These studies have used a variety of Tier 3 methods to quantify emissions of methane to the atmosphere (Alvarez, 2018).

Commerce in fuel generally involves the measurement of either fuel volume or mass at the point where statistics are collected, for example, liters of fuel oil or tons of coal. Conversion to mass units allows calorific value conversion factors to determine the amount of energy produced by combusting a unit fuel mass or volume, i.e., allowing computation of energy content in units of Terajoules. The fiill oxidization assumption is generally sound, particularly for stationary sources (power plants for example), where process equipment design supports it. Extensive determinations support the fact that CO, emission factors are relatively insensitive to the combustion process technology itself. As a result, estimated values based on emission factors representing a global mean are used in situations where more detailed information on the combustion process emissions may not be well known. However, because national situations may differ from a globally averaged value, particularly in situations where fiill oxidation may not be the case, TFI guidelines provide flexibility to account for a range of situations. Such cases could include the transportation sector, where incomplete combustion may occur across a vehicle fleet due to tradeoffs in the operation of internal combustion engines, in those cases where incomplete combustion is a consequence of the reduction of unwanted pollutants. Given the broad range of fossil fuel combustion conditions and the complete oxidation condition assumption, the TFI has provided a means to compensate for incomplete combustion cases via a tlnee-tiered structure that allows emission factor adjustment to more accurately reflect use conditions.

Tier 1 methods use nationally determined fuel consumption statistics and reference emissions factors published by the TFI Emission Factor Database (EFDB) (IPCC, 2018). These data reflect average properties of similar combustion systems. To facilitate use, the EFDB contains emission factor values based on global averages of energy produced by the combustion of fuel types, e.g., residual or other grades of fuel oils, shale oil, pulverized bituminous coal, or natural gas. Each of these fuel sub-types may have firing condition dependencies. The TFI Tier structure provides the means to adjust emission factor values according to more detailed fuel specifications that for a nation may differ from global averages, e.g., where combustion process conditions lie outside the range of conditions corresponding to EFDB values. In such cases. Tier 2 and Tier 3 methods are provided. Tier 2 methods use the same fuel consumption statistics as used in Tier 1 but may be combined with country-specific emission factors that more closely reflect that nation’s practice. In the use of the higher tier methods, IPCC notes that it is good practice in inventory compilation to provide well-documented information describing the methodology used to develop the emission factors used in developing a nation's yearly emission report.

Tier 3 methods allow the most latitude in determining emission factor and activity data. A nation may choose to include additional data or methods that more closely reflect its usage and practices. For example, a nation may have more accurate fuel characterization data based on conditions of use, or direct emissions measurement data for individual plants that may use Tier 3 methods. Use of energy content values for a fuel whose reference value may differ significantly from reference values due to national circumstances is another example where a nation may choose not to use EFDB values. Tier 3 methods may also account for uncombusted fuel leaving the combustion process, e.g.. coal found in ash based on ash carbon content analysis or emissions associated with the combustion process or plant-specific process, e.g., emissions of methane or nitrous oxide, or use of direct measurement with the Continuous Emissions Monitoring systems (CEMs), as is often the case in the U.S. (CEMs are described in a subsequent section of this chapter.) As mentioned above, the TFI guidelines stress the need to document procedures and data sources as good practice.

4.1.2 Emissions determinations via fuel calculations

Emission quantification via the fuel calculation method relies on fuel mass or volume and calorific value determination. Mass or volume quantification accuracy for fuel combusted in individual plants is largely dependent upon the capability, condition, and maintenance of the quantification equipment itself. When operating within manufacturer’s specifications, emissions control technologies generally operate with stated emissions performance. This may vary among plants due to maintenance and operations practices.

Fuel calorific value measurement of solid fuels depends upon several issues associated with the sampling methods. Sources of variability in the sampling of coal streams are complicated and influenced by mining and cleaning processes meant to remove extraneous, non-combustible materials. Relatively small (1 cm3 to 10 cm3) coal samples are required for calorific value measurement via complete carbon oxidation in an O, atmosphere. The means of obtaining relatively small samples representative of a large mass of solid fuel raises difficult to resolve questions. Potentially, the fuel that is fired may differ significantly from a sample drawn from the bulk of the fuel itself. These sources of uncertainty in calorific content of the mass of fuel finally burned are difficult to quantify without extensive auxiliary observations of the filing process that generally he far outside common practice.

Unlike solid fuels, quantification of natural gas volumes and calorific value determination can be accomplished with excellent accuracy for carbon management. Extensil e thermochemical property data (REFPROP, 2019) developed in recent decades has resulted in uncertainty in carbon content below 1% based upon determination of natural gas mixture composition. Gas composition information is routinely obtained from constituent analysis in natural gas distribution systems. Combustion equipment is designed to achieve complete fuel carbon oxidation using either gas turbine or gas combustor systems found primarily in electrical generation. This information supports high accuracy CO, emissions estimates based on the fuel calculation method for natural gas.

Direct emissions measurement—continuous emissions monitoring technology

Production of steam for industrial use, e.g., manufacturing of paper, chemical processing, or electricity generation, relies strongly on fossil fuel combustion. Steam boiler combustion control methods (Babcock and Wilcox, 2018) are longstanding and have been adapted to emissions quantification in the form of Continuous Emissions Monitoring Systems (OEMs). Historically, this measurement technology was used for acid gas emission control as mandated in the U.S. by the Clean Air Act amendments implemented in the early 1990s. CEMs technology is a bottom-up, tier 2 or 3 method as it utilizes emission process characteristics for quantification, directly measuring bulk flue gas flow rate and mole fraction of pertinent flue gases in a plant’s stack.

where: MC02 = mass flow of CO, (kg/s)

Xco, = mole fraction of CO„

Mco, = relative molecular mass of CO,,

M = relative molecular mass of CO„

air V

pco, = density or mass fraction of CO, (kg/m3),

p = an density (kg/m3), and

Qfiue = volumetric flow of flue gas emitted from the stack (m3/s).

Equation (3) relates the measured parameters to CO, mass flow and 3a uses the CO, mole fraction. As these measurements are always made in powerplant stacks, temperature and pressure measurements of the flue gas are required in order to properly determine the gas densities involved.

CEMs were originally equipped with SO, and NOx mole fraction measurement channels. The advent of CO, reporting requirements resulted in the addition of another mole fraction measurement channel to existing CEMs installations as a cost-effective means of CO, emissions self-reporting by individual power plants. For CEMs methods flue gases are continuously sampled for real-time analysis, primarily using uon-dispersive infrared (NDIR) analyzers periodically calibrated with gas mixtures of known mole fraction values. (A description of NDIR technology is given in a subsequent section of this chapter.) To ensure consistency in the accuracy of flue gas mole fraction analysis across the emissions reporting system, EPA requires that these calibration gases be traceable to U.S. national measurement standards and used at specific frequencies in field installations. NIST supports accurate component concentration measurements by providing certified gas mixtures via the NIST Traceable Reference Materials Program (NIST-NTRM, 2019). The NTRM program works with specialty gas manufacturers to supply standards of certified accuracy to plants and the stack testing community. NTRM reference gas mixtures are traceable to NIST primary mole fraction standards over specific ranges of field use at uncertainty levels well below the 2% EPA requirement. The specialty gas industiy uses NTRMs as internal working standards in its field standard certification procedures. These practices indicate that flue component mole fraction quantification may contribute uncertainty in CEMs quantification of CO, in stack gases, but that contribution is likely significantly below я 20% seen in comparing fuel calculation/CEMs-based emissions.

Comparing fuel calculation and CEMs measurements

Emissions reporting data compilation for stationary sources may use either of two methods, direct measurement (Tier 2 or 3) or estimation of emissions based on the fuel calculation method (Tier 1). Most U.S. electrical generation plants are equipped with CEMs technology for direct CO, emissions measurement. These plants also report the amount of energy produced and fuel used to the U.S. Energy Information Administr ation (EIA). Several comparisons investigating differences arising from use of the two methods for individual plants within U.S. plants have been published (Ackerman, 2008; Borthwick, 2011; Quick, 2013; Gurney, 2016). Although the average difference for all is reported to be 1 to я 3%, individual generation plant values reportedly differed by as much as 25%. Figure 2 illustrates data taken from publicly available EPA and EIA databases for approximately 800 plants using a range of solid fuels. Although the distribution is not Gaussian, for the purposes of estimation, a Gaussian distribution, shown as the red curve, was used as a simple way to approximate a mean difference of я 1.5% and a half-width of я 15% (Borthwick, 2011). These studies raised questions concerning the accuracy of both methods. Since mole fraction measurements in U.S. power plant stacks must demonstrate traceability to NIST mole fraction standards at the 2% level or below, bulk flue gas flow determination accuracy was investigated in order to determine whether its measurement may be a contributing factor to these differences at levels comparable to that of fuel calculation methods.

Indtvtdual plant emissrons differences, CEMs/Fuel calculation (Borthwick, 2011)

Figure 2. Indtvtdual plant emissrons differences, CEMs/Fuel calculation (Borthwick, 2011).

Flue gas flow rate measurement

Uncertainty in stack flue gas flow measurement is the other CEMs parameter to consider as a possible source of the difference between fuel calculation and CEMs methods. Stack flow measurement technology has evolved over the last several decades with the advent and application of non-intrusive, ultrasonic flow metering technology. It has become the dominant stack flow measurement method in the U.S., with installations using either one or two ultrasonic flow meters (USMs), the latter usually in a crossed beam configuration. Of the two CEMs parameters, it may have the greatest impact as it affects the calculation of emitted trace constituent mass for all components, e.g., CO,, SO, or NCK

AUSM is composed of two ultrasonic transceivers mounted on either side of a stack. In operation, one USM transceiver (consider the red transceiver pair in Figure 3) transmits ultrasound to the meter’s other transceiver, USM la to b, along a diametral chord across the stack as shown. This chord is arranged at a fixed inclination angle, down, measured pathlength between the transceivers, and inclination angle value, both the average flow velocity and the speed of sound in the gas can be calculated.

Recent research (Johnson, 2019; Bryant, 2018) and testing results have shown that this approach can achieve accuracy levels of ~ 1% for the crossed-beam configuration shown in Figure 3. When only a single meter was used in these tests to calculate flow velocity, errors in the range of 10% to 17% occurred, depending upon flow velocity. These performance levels have been obtained in NIST flow testing systems that emulate stack flow characteristics, i.e., strongly asymmetric axial velocity profiles, significant cross-axis velocities (swirl or turbulence), over axial velocity' ranges normally found in stacks, up to a 25 m/s. Flow standards used in these investigations are traceable to U.S. national standards having uncertainties of ~ 0.7%. Some industrial stacks in the U.S. are equipped with crossed-beam systems. Currently, collaborative efforts between NIST and industry partners are investigating crossed- beam method performance in stacks of coal-fired electrical generating plants in order to assess their

Transceiver arrangement and velocity vectors for two ultrasonic flow meters configured in a crossed beam metering configuration

Figure 3. Transceiver arrangement and velocity vectors for two ultrasonic flow meters configured in a crossed beam metering configuration. Diagram courtesy of A. Johnson, NIST, performance in industrial settings. This assessment is anticipated to result in similar results in coal-fired plant stacks. Use of the cross-beam method can significantly improve accuracy if applied through coal- fired plants.

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  • [7] The presumption is that the vehicles are internal combustion engine-driven vehicles. As electric vehicles continue topenetrate the marketplace, a means of distinguishing between the two will likely be needed in order to more accuratelydiscriminate between emitting and non-emitting vehicles.
  • [8]
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