Equivalent leak definitions for Smart LDAR (leak detection and repair) when using optical imaging technology

Controlling fugitive emissions from leaks in petrochemical industry process equipment now requires periodic monitoring of valves, flanges, pumps etc., typically on a quarterly basis. Previous studies have shown that over 90% of the reducible emissions come from approximately 0.1% of the components, i.e. the large leakers. A new, and more cost-effective approach for controlling these large leakers would entail more frequent monitoring of process equipment, allowing for the detection and repair of the highly leaking components that contribute the most to emissions. This approach has been called "Smart LDAR." New optical imaging instruments, which significantly reduce monitoring costs, are now available to implement such an alternative work practice. This work describes the determination of the leak detection sensitivity (equivalency threshold) that an optical imaging instrument must achieve to ensure that it will provide at least the equivalent emission control of the current leak detection and repair practice. Equivalency thresholds were developed for various monitoring intervals. The U.S. Environment Protection Agency's Monte Carlo simulation approach was used to perform the analysis and to demonstrate that optical imaging, which is capable of identifying all of the largest leakers, can provide better control of fugitive emissions.     

Characterization of the fugitive particulate emissions from construction mud/dirt carryout

Although the fugitive dust associated with construction mud/dirt carryout can represent a substantial portion of the particulate matter (PM) emissions inventory in nonattainment areas, it has not been well characterized by direct sampling methods. In this paper, a research program is described that directly determined both PM10 and PM2.5 (particles < or =10 and 2.5 microm in classical aerodynamic diameter, respectively) emission factors for mud/dirt carryout from a major construction project located in metropolitan Kansas City, MO. The program also assessed the contribution of automotive emissions to the total PM2.5 burden and determined the baseline emissions from the test road. As part of the study, both time-integrated and continuous exposure-profiling methods were used to assess the PM emissions, including particle size and elemental composition. This research resulted in overall PM10 and PM2.5 emission factors of 6 and 0.2 g/vehicle, respectively. Although PM10 is within the range of prior U.S. Environmental Protection Agency (EPA) guidance, the PM2.5 emission factor is far lower than previous estimates published by EPA. In addition, based on both the particle size and chemical data obtained in the study, a major portion of the PM2.5 emissions appears to be attributable to automotive exhaust from light-duty, gasoline-powered vehicles and not to the fugitive dust associated with reentrained mud/dirt carryout.     

Detection limits of optical gas imagers as a function of temperature differential and distance

Optical gas imaging (OGI) is an effective tool for detecting gas leaks from process equipment. Despite the fact that OGI has been used for leak detection for over a decade, its detection limit is an elusive performance metric and has not been systematically characterized and quantified like other detection instruments. A substantial body of research has been performed that has shed some light on the OGI detection limits and the factors that dictate the detection limits. The OGI detection limit expressed as ppm-m and ppm now can be quantified as a function of ΔT (differential temperature between the gas plume and the background), the OGI camera type, and the specific gas in question. Furthermore, the OGI detection limit expressed as grams per hour can be calculated based on the ΔT and the distance from the OGI camera to the leak location under common leak survey conditions. For the same OGI camera, the detection limit can vary by several orders of magnitude due to ΔT and distance. The present work has demonstrated how different OGI detection limits can be. More importantly, this work has, for the first time, formulated equations that can be used to determine OGI detection limits with a given set of leak detection conditions. Being able to quantify OGI detection limit and understand the variables that dictate the detection limit is a significant advancement. It will help OGI to become accepted as a mature field instrument. The variables characterized in this work should have an impact on the development of OGI leak survey protocols, such as Appendix K to Code of Federal Regulations 40 CFR Part 60 in the United States. Established detection limits will also help emission inventory for fugitive emissions when OGI is used as the sole leak detection method.

Implications: Optical gas imaging (OGI) has been used for leak detection and control of fugitive volatile organic compound (VOC) emissions and methane emissions due to equipment leaks. However, detection limits of OGI have not been characterized and quantified like other detection instruments. The lack of well-understood detection limits has hindered broader applications of OGI. The work presented in this paper represents important steps that will enable OGI users and policymakers to establish (1) OGI detection limits under various conditions, (2) OGI leak survey criteria for a desired minimum detectable leak size, and (3) maximum potential emissions from the nondetect sources in emission inventory studies.     

Characterization of the Fugitive Particulate Emissions from Construction Mud/Dirt Carryout

Abstract

Although the fugitive dust associated with construction mud/dirt carryout can represent a substantial portion of the particulate matter (PM) emissions inventory in non-attainment areas, it has not been well characterized by direct sampling methods. In this paper, a research program is described that directly determined both PM₁₀ and PM_2.5 (particles ≤10 and 2.5 μm in classical aerodynamic diameter, respectively) emission factors for mud/dirt carryout from a major construction project located in metropolitan Kansas City, MO. The program also assessed the contribution of automotive emissions to the total PM_2.5 burden and determined the baseline emissions from the test road. As part of the study, both time-integrated and continuous exposure-profiling methods were used to assess the PM emissions, including particle size and elemental composition. This research resulted in overall PM₁₀ and PM_2.5 emission factors of 6 and 0.2 g/vehicle, respectively. Although PM₁₀ is within the range of prior U.S. Environmental Protection Agency (EPA) guidance, the PM_2.5 emission factor is far lower than previous estimates published by EPA. In addition, based on both the particle size and chemical data obtained in the study, a major portion of the PM_2.5 emissions appears to be attributable to automotive exhaust from light-duty, gasoline-powered vehicles and not to the fugitive dust associated with re-entrained mud/dirt carryout.     

Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain

The photochemical grid model, UAM-V, has been used by regulatory agencies to make decisions concerning emissions controls, based on studies of the July 1995 ozone episode in the eastern US. The current research concerns the effect of the uncertainties in UAM-V input variables (emissions, initial and boundary conditions, meteorological variables, and chemical reactions) on the uncertainties in UAM-V ozone predictions. Uncertainties of 128 input variables have been estimated and most range from about 20% to a factor of two. 100 Monte Carlo runs, each with new resampled values of each of the 128 input variables, have been made for given sets of median emissions assumptions. Emphasis is on the maximum hourly-averaged ozone concentration during the 12–14 July 1995 period. The distribution function of the 100 Monte Carlo predicted domain-wide maximum ozone concentrations is consistently close to log-normal with a 95% uncertainty range extending over plus and minus a factor of about 1.6 from the median. Uncertainties in ozone predictions are found to be most strongly correlated with uncertainties in the NO₂ photolysis rate. Also important are wind speed and direction, relative humidity, cloud cover, and biogenic VOC emissions. Differences in median predicted maximum ozone concentrations for three alternate emissions control assumptions were investigated, with the result that (1) the suggested year-2007 emissions changes would likely be effective in reducing concentrations from those for the year-1995 actual emissions, that (2) an additional 50% NO_x emissions reductions would likely be effective in further reducing concentrations, and that (3) an additional 50% VOC emission reductions may not be effective in further reducing concentrations.     

A Method for Rapid Calculation of Merchant Vessel Combustion Emission

A simple method for calculating long term (1 year average) combustion emissions from cargo vessels is presented. The method uses emission factors for the five EPA criteria pollutants based on the amount and type of cargo carried by these vessels. Separate emission factors are derived for the underway and docking operations based on current fuel consumption to transport or load/unload cargo. Fuel consumption is then converted to emission factors in the form of tons of pollutant per million ton miles of cargo transported or tons of pollutant per million tons of cargo loaded/unloaded. The emission calculations can be performed on a desk calculator using data published in a federal document. The annual emissions of the Port of Houston are estimated as a sample calculation. The report also Includes a method of determining the spatial distributions of the emissions and a listing of stack parameters.     

Dust emission factors for environment of Northern China

This paper gives the dust emission inventory in the Northern China where the climate is very dry and large desert areas exist. Before calculating the distribution of fugitive dust emission factors (emission rates of particles smaller than 0.05 mm in diameter) from natural surfaces with a US EPA formula, Chinese data of pedology and climatology were processed so as to suit requirements of the formula. The computed dust emission factors for this environment of Northern China are shown by contours, their distribution and seasonal variations are briefly discussed. The dust emission rate in the area increases from east to west by five orders. Also, the total amount of the dust emitted from natural surfaces of Northern China into the atmosphere is found to be some 43 million t yr^-1, with half of the emissions concentrated in the spring season (March–May).     

Analysis of the number of flux chamber samples and study area size on the accuracy of emission rate measurements

Monte Carlo simulations were conducted on a set of flux chamber measurements at a landfill to estimate the relationship between the number of flux chamber samples and study area size on the emission rate measurement accuracy. The spatial variability of flux was addressed in the study by utilizing an existing flux chamber measurement data set that is one of the most dense flux chamber sampling arrays published to date for a landfill. At a probability of 95%, the Monte Carlo simulations indicated that achieving an accuracy within 10% with the flux chamber method is highly unlikely. An accuracy within 20% was achieved for small areas of less than about 0.2 hectares using 220 flux chamber measurements, but achieving this level of accuracy for area emission sources, of similar or greater variability, that are larger than this is highly unlikely. An accuracy within 30% was achieved up to the Full Area of about 0.4 hectares if more than approximately 120 samples were obtained. Even for an accuracy within 50%, at least 40 flux chamber measurements were needed for the Full Area of about 0.4 hectares. Available methods of estimating the number of samples required were compared to the Monte Carlo simulation results. The Monte Carlo simulations indicate that, in general, more samples are required than determined from an existing statistical method, which is a function of the mean and standard deviation of the population. Specifying the number of samples based on a regulatory method results in very poor accuracy. A modification to the statistical method for estimating the number of samples, or for estimating an accuracy for a given probability and number of samples, is proposed.

Implications: The flux chamber method is the most widely used method of measuring fugitive emission rates from area sources. However, extrapolation of a set of individual flux chamber samples to a larger area results in area flux measurement values of unknown accuracy. Quantification of the accuracy of the extrapolation of a set of flux chamber measurements would be beneficial for understanding the confidence that can be placed on the measurement results. Guidance as to the appropriate number of flux chamber measurements to achieve a desired level of accuracy would benefit flux chamber method practitioners.     

Development of a country-specific CO2 emission factor for domestic anthracite in Korea, 2007�C2009

Introduction

Korea has been making efforts to reduce greenhouse gas (GHG) emissions, including a voluntary commitment to the target of a 30% reduction, based on business-as-usual of the total GHG emission volume, by 2020; 2006 IPCC Guidelines provided default values, applying country-specific emission factors was recommended when estimating national greenhouse gas emissions.

Results and discussion

This study focused on anthracite produced in Korea in order to provide basic data for developing country-specific emission factor. This study has estimated CO₂ emission factors to use worksheet of which five steps consisted according to the fuel analysis method.

Conclusion

As a result, the average of net colorific value for 3 years (2007??2009) was 4,519 kcal/kg, and the CO₂ emission factor was calculated to be 111,446 kg/TJ, which is about 11.8% lower than the 2006 IPCC guidelines default value, and about 7.9% higher than the US EPA emission factor.     

Methods to determine response factors for infrared gas imagers used as quantitative measurement devices

Response factors (RF) can be used to characterize relative sensitivity of one compound vs. another compound for the same measurement instrument. Use of RF allows the analysts/operators to calibrate the instrument with one compound and make measurement for a large number of compounds. This method is adopted for Flame Ionization Detector (FID) based survey instruments used in the Leak Detection and Repair (LDAR) practice for control of fugitive emissions of volatile organic compounds. Gas detecting Infrared (IR) cameras have been used for leak detection. However, the RF for IR cameras has not been well established despite some attempt to develop a method for IR camera RF. In addition to a method proposed earlier (Method 1), two new methods for IR camera RF are proposed in this paper: Method 2 based on theoretical approach and Method 3 based on experimental approach. All three methods are examined and compared. Both Methods 2 and 3 have shown the ability to characterize the behavior of RF for various compounds and substantially higher accuracy than Method 1. Method 2 provides a mechanism to generate RF for a large number of compounds without conducting experiments, and is recommended for implementation. The RF derived from this method can be used both in the emerging field of Quantitative Optical Gas Imaging (QOGI) and to answer the most common question that IR camera users ask—whether a particular compound can be imaged by a particular IR camera.Implications: Infrared imager is an efficient tool for detecting gas leaks from process equipment and has been used in leak detection and repair (LDAR) programs for control of fugitive emissions. However, the information regarding which chemical compounds can be imaged and how sensitive a given infrared imager is for various compounds is limited. A theoretical method is presented in this paper that can answer these questions without conducting resource-intensive experiment. The results of this theoretical method has good agreement with experimental data. The method has been used to predict relative sensitivity for 398 compounds.     

Evaluation of pollution prevention options to reduce styrene emissions from fiber-reinforced plastic open molding processes

Pollution prevention (P2) options to reduce styrene emissions, such as new materials and application equipment, are commercially available to the operators of open molding processes. However, information is lacking on the emissions reduction that these options can achieve. To meet this need, the U.S. Environmental Protection Agency's (EPA) Air Pollution Prevention and Control Division, working in collaboration with Research Triangle Institute, measured styrene emissions for several of these P2 options. In addition, the emission factors calculated from these test results were compared with the existing EPA emission factors for gel coat sprayup and resin applications. Results show that styrene emissions can be reduced by up to 52% by using controlled spraying (i.e., reducing overspray), low-styrene and styrene-suppressed materials, and nonatomizing application equipment. Also, calculated emission factors were 1.6-2.5 times greater than the mid-range EPA emission factors for the corresponding gel coat and resin application. These results indicate that facilities using existing EPA emission factors to estimate emissions in open molding processes are likely to underestimate actual emissions. Facilities should investigate the applicability and feasibility of these P2 options to reduce their styrene emissions.     

Comparing estimates of fugitive landfill methane emissions using inverse plume modeling obtained with Surface Emission Monitoring (SEM), Drone Emission Monitoring (DEM), and Downwind Plume Emission Monitoring (DWPEM)

ABSTRACT

As part of the global effort to quantify and manage anthropogenic greenhouse gas emissions, there is considerable interest in quantifying methane emissions in municipal solid waste landfills. A variety of analytical and experimental methods are currently in use for this task. In this paper, an optimization-based estimation method is employed to assess fugitive landfill methane emissions. The method combines inverse plume modeling with ambient air methane concentration measurements. Three different measurement approaches are tested and compared. The method is combined with surface emission monitoring (SEM), above ground drone emission monitoring (DEM), and downwind plume emission monitoring (DWPEM). The methodology is first trialed and validated using synthetic datasets in a hand-generated case study. A field study is also presented where SEM, DEM and DWPEM are tested and compared. Methane flux during two-days measurement campaign was estimated to be between 228 and 350 g/s depending on the type of measurements used. Compared to SEM, using unmanned aerial systems (UAS) allows for a rapid and comprehensive coverage of the site. However, as showed through this work, advancement of DEM-based methane sampling is governed by the advances that could be made in UAS-compatible measurement instrumentations. Downwind plume emission monitoring led to a smaller estimated flux compared with SEM and DEM without information about positions of major leak points in the landfill. Even though, the method is simple and rapid for landfill methane screening. Finally, the optimization-based methodology originally developed for SEM, shows promising results when it is combined with the drone-based collected data and downwind concentration measurements. The studied cases also discovered the limitations of the studied sampling strategies which is exploited to identify improvement strategies and recommendations for a more efficient assessment of fugitive landfill methane emissions.

Implications: Fugitive landfill methane emission estimation is tackled in the present study. An optimization-based method combined with inverse plume modeling is employed to treat data from surface emission monitoring, drone-based emission monitoring and downwind plume emission monitoring. The study helped revealing the advantages and the limitations of the studied sampling strategies. Recommendations for an efficient assessment of landfill methane emissions are formulated. The method trialed in this study for fugitive landfill methane emission could also be appropriate for rapid screening of analogous greenhouse gas emission hotspots.     

Autonomous mobile platform for monitoring air emissions from industrial and municipal wastewater ponds

Significant amounts of volatile organic compounds and greenhouse gases are generated from wastewater lagoons and tailings ponds in Alberta, Canada. Accurate measurements of these air pollutants and greenhouse gases are needed to support management and regulatory decisions. A mobile platform was developed to measure air emissions from tailings pond in the oil sands region of Alberta. The mobile platform was tested in 2015 in a municipal wastewater treatment lagoon. With a flux chamber and a CO₂/CH₄ sensor on board, the mobile platform was able to measure CO₂ and CH₄ emissions over two days at two different locations in the pond. Flux emission rates of CO₂ and CH₄ that were measured over the study period suggest the presence of aerobic and anaerobic zones in the wastewater treatment lagoon. The study demonstrated the capabilities of the mobile platform in measuring fugitive air emissions and identified the potential for the applications in air and water quality monitoring programs.

Implications: The Mobile Platform demonstrated in this study has the ability to measure greenhouse gas (GHG) emissions from fugitive sources such as municipal wastewater lagoons. This technology can be used to measure emission fluxes from tailings ponds with better detection of spatial and temporal variations of fugitive emissions. Additional air and water sampling equipment could be added to the mobile platform for a broad range of air and water quality studies in the oil sands region of Alberta.     

Deriving fuel-based emission factor thresholds to interpret heavy-duty vehicle roadside plume measurements

Remote sensing devices have been used for decades to measure gaseous emissions from individual vehicles at the roadside. Systems have also been developed that entrain diluted exhaust and can also measure particulate matter (PM) emissions. In 2015, the California Air Resources Board (CARB) reported that 8% of in-field diesel particulate filters (DPF) on heavy-duty (HD) vehicles were malfunctioning and emitted about 70% of total diesel PM emissions from the DPF-equipped fleet. A new high-emitter problem in the heavy-duty vehicle fleet had emerged. Roadside exhaust plume measurements reflect a snapshot of real-world operation, typically lasting several seconds. In order to relate roadside plume measurements to laboratory emission tests, we analyzed carbon dioxide (CO₂), oxides of nitrogen (NO_X), and PM emissions collected from four HD vehicles during several driving cycles on a chassis dynamometer. We examined the fuel-based emission factors corresponding to possible exceedances of emission standards as a function of vehicle power. Our analysis suggests that a typical HD vehicle will exceed the model year (MY) 2010 emission standards (of 0.2 g NO_X/bhp-hr and 0.01 g PM/bhp-hr) by three times when fuel-based emission factors are 9.3 g NO_X/kg fuel and 0.11 g PM/kg using the roadside plume measurement approach. Reported limits correspond to 99% confidence levels, which were calculated using the detection uncertainty of emissions analyzers, accuracy of vehicle power calculations, and actual emissions variability of fixed operational parameters. The PM threshold was determined for acceleration events between 0.47 and 1.4 mph/sec only, and the NO_X threshold was derived from measurements where after-treatment temperature was above 200°C. Anticipating a growing interest in real-world driving emissions, widespread implementation of roadside exhaust plume measurements as a compliment to in-use vehicle programs may benefit from expanding this analysis to a larger sample of in-use HD vehicles.

Implications: Regulatory agencies, civil society, and the public at large have a growing interest in vehicle emission compliance in the real world. Leveraging roadside plume measurements to identify vehicles with malfunctioning emission control systems is emerging as a viable new and useful method to assess in-use performance. This work proposes fuel-based emission factor thresholds for PM and NOx that signify exceedances of emission standards on a work-specific basis by analyzing real-time emissions in the laboratory. These thresholds could be used to prescreen vehicles before roadside enforcement inspection or other inquiry, enhance and further develop emission inventories, and potentially develop new requirements for heavy-duty inspection and maintenance (I/M) programs, including but not limited to identifying vehicles for further testing.     

Particulate emissions from U.S. Department of Defense artillery backblast testing

There is a dearth of information on dust emissions from sources that are unique to the U.S. Department of Defense testing and training activities. However, accurate emissions factors are needed for these sources so that military installations can prepare accurate particulate matter (PM) emission inventories. One such source, coarse and fine PM (PM10 and PM2.5) emissions from artillery backblast testing on improved gun positions, was characterized at the Yuma Proving Ground near Yuma, AZ, in October 2005. Fugitive emissions are created by the shockwave from artillery pieces, which ejects dust from the surface on which the artillery is resting. Other contributions of PM can be attributed to the combustion of the propellants. For a 155-mm howitzer firing a range of propellant charges or zones, amounts of emitted PM10 ranged from -19 g of PM10 per firing event for a zone 1 charge to 92 g of PM10 per firing event for a zone 5. The corresponding rates for PM2.5 were approximately 9 g of PM2.5 and 49 g of PM2.5 per firing. The average measured emission rates for PM1o and PM2.5 appear to scale with the zone charge value. The measurements show that the estimated annual contributions of PM10 (52.2 t) and PM2.5 (28.5 t) from artillery backblast are insignificant in the context of the 2002 U.S. Environment Protection Agency (EPA) PM emission inventory. Using national-level activity data for artillery fire, the most conservative estimate is that backblast would contribute the equivalent of 5 x 10(-4) % and 1.6 x 10(-3)% of the annual total PM10 and PM2.5 fugitive dust contributions, respectively, based on 2002 EPA inventory data.     

Direct Measurement of Fugitive Emissions of Hydrocarbons from a Refinery

Abstract

Refineries are a source of emissions of volatile hydrocarbons that contribute to the formation of smog and ozone. Fugitive emissions of hydrocarbons are difficult to measure and quantify. Currently these emissions are estimated based on standard emission factors for the type and use of equipment installed. Differential absorption light detection and ranging (DIAL) can remotely measure concentration profiles of hydrocarbons in the atmosphere up to several hundred meters from the instrument. When combined with wind speed and direction, downwind vertical DIAL scans can be used to calculate mass fluxes of the measured gas leaving the site. Using a mobile DIAL unit, a survey was completed at a Canadian refinery to quantify fugitive emissions of methane, C₂₊ hydrocarbons, and benzene and to apportion the hydrocarbon emissions to the various areas of the refinery. Refinery fugitive emissions as measured with DIAL during this demonstration study were 1240 kg/hr of C₂₊ hydrocarbons, 300 kg/hr of methane, and 5 kg/hr of benzene. Storage tanks accounted for over 50% of the total emissions of C₂₊ hydrocarbons and benzene. The coker area and cooling towers were also significant sources. The C₂₊ hydrocarbons emissions measured during the demonstration amounted to 0.17% of the mass of the refinery hydrocarbon throughput for that period. If the same loss were repeated throughout the year, the lost product would represent a value of US$3.1 million/yr (assuming US$40/bbl). The DIAL-measured hourly emissions of C₂₊ hydrocarbons were 15 times higher than the emission factor estimates and gave a different perspective on which areas of the refinery were the main source of emissions. Methods, such as DIAL, that can directly measure fugitive emissions would improve the effectiveness of efforts to reduce emissions, quantify the reduction in emissions, and improve the accuracy of emissions data that are reported to regulators and the public.     

An environmental decision-making tool for evaluating ground-level ozone-related health effects

A computer model called the Ozone Risk Assessment Model (ORAM) was developed to evaluate the health effects caused by ground-level ozone (O3) exposure. ORAM was coupled with the U.S. Environmental Protection Agency's (EPA) Third-Generation Community Multiscale Air Quality model (Models-3/CMAQ), the state-of-the-art air quality model that predicts O3 concentration and allows the examination of various scenarios in which emission rates of O3 precursors (basically, oxides of nitrogen [NOx] and volatile organic compounds) are varied. The principal analyses in ORAM are exposure model performance evaluation, health-effects calculations (expected number of respiratory hospital admissions), economic valuation, and sensitivity and uncertainty analysis through a Monte Carlo simulation. As a demonstration of the system, ORAM was applied to the eastern Tennessee region, and the entire O3 season was simulated for a base case (typical emissions) and three different emission scenarios. The results indicated that a synergism occurs when reductions in NOx emissions from mobile and point sources were applied simultaneously. A 12.9% reduction in asthma hospital admissions is expected when both mobile and point source NOx emissions are reduced (50 and 70%, respectively) versus a 5.8% reduction caused by mobile source and a 3.5% reduction caused by point sources when these emission sources are reduced individually.     

Use of Subsampled Traffic Data to Estimate Roadway Emissions,Including Conversion to MOBILE6 Vehicle Classifications

Abstract

Air quality is degraded by many factors, among which the emissions from on‐road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy‐making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NO_x), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NO_x emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

Emissions Variability Processor (EMVAP): Design,evaluation, and application

Emissions of pollutants such as SO₂ and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration predictions. This procedure uses emissions varied hourly with a Monte Carlo sampling process that is based upon the user-specified emissions distribution, from which a probabilistic estimate can be obtained of the controlling concentration. EMVAP can also accommodate an advanced Tier 2 NO₂ modeling technique that uses a varying ambient ratio method approach to determine the fraction of total oxides of nitrogen that are in the form of nitrogen dioxide. For the case of the 1-hr National Ambient Air Quality Standards (NAAQS, established for SO₂ and NO₂), a “critical value” can be defined as the highest hourly emission rate that would be simulated to satisfy the standard using air dispersion models assuming constant emissions throughout the simulation. The critical value can be used as the starting point for a procedure like EMVAP that evaluates the impact of emissions variability and uses this information to determine an appropriate value to use for a longer term (e.g., 30-day) average emission rate that would still provide protection for the NAAQS under consideration. This paper reports on the design of EMVAP and its evaluation on several field databases that demonstrate that EMVAP produces a suitably modest overestimation of design concentrations. We also provide an example of an EMVAP application that involves a case in which a new emission limitation needs to be considered for a hypothetical emission unit that has infrequent higher-than-normal SO₂ emissions.

ImplicationsEmissions of pollutants from combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup and shutdown. While monitoring will automatically reflect this variability on measured concentrations, dispersion modeling is typically conducted with a single peak emission rate assumed to occur continuously. To realistically account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, the authors have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates.     

Direct measurement of fugitive emissions of hydrocarbons from a refinery

Refineries are a source of emissions of volatile hydrocarbons that contribute to the formation of smog and ozone. Fugitive emissions of hydrocarbons are difficult to measure and quantify. Currently these emissions are estimated based on standard emission factors for the type and use of equipment installed. Differential absorption light detection and ranging (DIAL) can remotely measure concentration profiles of hydrocarbons in the atmosphere up to several hundred meters from the instrument. When combined with wind speed and direction, downwind vertical DIAL scans can be used to calculate mass fluxes of the measured gas leaving the site. Using a mobile DIAL unit, a survey was completed at a Canadian refinery to quantify fugitive emissions of methane, C2+ hydrocarbons, and benzene and to apportion the hydrocarbon emissions to the various areas of the refinery. Refinery fugitive emissions as measured with DIAL during this demonstration study were 1240 kg/hr of C2+ hydrocarbons, 300 kg/hr of methane, and 5 kg/hr of benzene. Storage tanks accounted for over 50% of the total emissions of C2+ hydrocarbons and benzene. The coker area and cooling towers were also significant sources. The C2+ hydrocarbons emissions measured during the demonstration amounted to 0.17% of the mass of the refinery hydrocarbon throughput for that period. If the same loss were repeated throughout the year, the lost product would represent a value of US$3.1 million/yr (assuming US$40/bbl). The DIAL-measured hourly emissions of C2+ hydrocarbons were 15 times higher than the emission factor estimates and gave a different perspective on which areas of the refinery were the main source of emissions. Methods, such as DIAL, that can directly measure fugitive emissions would improve the effectiveness of efforts to reduce emissions, quantify the reduction in emissions, and improve the accuracy of emissions data that are reported to regulators and the public.