Methane emissions from 20 landfills across the United States using vertical radial plume mapping

Landfill fugitive methane emissions were quantified as a function of climate type and cover type at 20 landfills using U.S. Environmental Protection Agency (EPA) Other Test Method (OTM)-10 vertical radial plume mapping (VRPM) with tunable diode lasers (TDLs). The VRPM data were initially collected as g CH₄/sec emission rates and subsequently converted to g CH₄/m²/day rates using two recently published approaches. The first was based upon field tracer releases of methane or acetylene and multiple linear regression analysis (MLRM). The second was a virtual computer model that was based upon the Industrial Source Complex (ISC3) and Pasquill plume stability class models (PSCMs). Calculated emission results in g CH₄/m²/day for each measured VRPM with the two approaches agreed well (r ² = 0.93). The VRPM data were obtained from the working face, temporary soil, intermediate soil, and final soil or synthetic covers. The data show that methane emissions to the atmosphere are a function of climate and cover type. Humid subtropical climates exhibited the highest emissions for all cover types at 207, 127, 102, and 32 g CH₄/m²/day, for working face (no cover), temporary, intermediate, and final cover, respectively. Humid continental warm summers showed 67, 51, and 27 g CH₄/m²/day for temporary, intermediate, and final covers. Humid continental cool summers were 135, 40, and 26 g CH₄/m²/day for the working face, intermediate, and final covers. Mediterranean climates were examined for intermediate and final covers only and found to be 11 and 6 g CH₄/m²/day, respectively, whereas semiarid climates showed 85, 11, 3.7, and 2.7 g CH₄/m²/day for working face, temporary, intermediate, and final covers. A closed, synthetically capped landfill covered with soil and vegetation with a gas collection system in a humid continental warm summer climate gave mostly background methane readings and average emission rates of only 0.09 g CH₄/m²/day flux when measurable.

Implications The OTM-10 method is being proposed by EPA to quantify surface methane emissions from landfill covers. This study of 20 landfills across the United States was done to determine the efficacy of using OTM-10 for this purpose. Two recently published models were used to evaluate the methane flux results found with VRPM optical remote sensing. The results should provide a sense of the practicality of the method, its limitations at landfills, and the impact of climate upon the cover's methane flux. Measured field data may assist landfill owners in refining previously modeled methane emission factor default values.     

A new approach to characterize emission contributions from area sources during optical remote sensing technique testing

In the method termed “Other Test Method-10,” the U.S. Environmental Protection Agency has proposed a method to quantify emissions from nonpoint sources by the use of vertical radial plume mapping (VRPM) technique. The surface area of the emitting source and the degree to which the different zones of the emitting source are contributing to the VRPM computed emissions are often unknown. The objective of this study was to investigate and present an approach to quantify the unknown emitting surface area that is contributing to VRPM measured emissions. Currently a preexisting model known as the “multiple linear regression model,” which is described in Thoma et al. (2009 Thoma, E.R., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M. and Hashmonay, R. 2010. Development of EPA OTM-10 for landfill applications. J. Environ. Eng., 136: 769–776. [Crossref], [Web of Science ®] , [Google Scholar]), is used for quantifying the unknown surface area.

The method investigated and presented in this paper utilized tracer tests to collect data and develop a model much like that described in Thoma et al. (2009 Thoma, E.R., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M. and Hashmonay, R. 2010. Development of EPA OTM-10 for landfill applications. J. Environ. Eng., 136: 769–776. [Crossref], [Web of Science ®] , [Google Scholar]). However, unlike the study used for development of the multiple linear regression model, this study is considered a very limited study due to the low number of pollutant releases performed (seven total releases). It was found through this limited study that the location of an emitting source impacts VRPM computed emissions exponentially, rather than linearly (i.e., the impact that an emitting source has on VRPM measurements decreases exponentially with increasing distances between the emitting source and the VRPM plane). The data from the field tracer tests were used to suggest a multiple exponential regression model. The findings of this study, however, are based on a very small number of tracer tests. More tracer tests performed during all types of climatic conditions, terrain conditions, and different emissions geometries are still needed to better understand the variation of capture efficiency with emitting source location. This study provides a step toward such an objective.

Implications The findings of this study will aid in the advancement of the VRPM technique. In particular, the contribution of this study is to propose a slight improvement in how the area contributing to flux is determined during VRPM campaigns. This will reduce some of the technique's inherent uncertainties when it is employed to estimate emissions from an area source under nonideal conditions.     

Methane emissions from 20 landfills across the United States using vertical radial plume mapping

Landfill fugitive methane emissions were quantified as a function of climate type and cover type at 20 landfills using US. Environmental Protection Agency (EPA) Other Test Method (OTM)-10 vertical radial plume mapping (VRPM) with tunable diode lasers (TDLs). The VRPM data were initially collected as g CH4/sec emission rates and subsequently converted to g CH4/m2/ day rates using two recently published approaches. The first was based upon field tracer releases of methane or acetylene and multiple linear regression analysis (MLRM). The second was a virtual computer model that was based upon the Industrial Source Complex (ISC3) and Pasquill plume stability class models (PSCMs). Calculated emission results in g CH4/m2/day for each measured VRPM with the two approaches agreed well (r2 = 0.93). The VRPM data were obtained from the working face, temporary soil, intermediate soil, and final soil or synthetic covers. The data show that methane emissions to the atmosphere are a function of climate and cover type. Humid subtropical climates exhibited the highest emissions for all cover types at 207, 127, 102, and 32 g CH4/m2/day, for working face (no cover), temporary, intermediate, and final cover, respectively. Humid continental warm summers showed 67, 51, and 27 g CH4/m2/day for temporary, intermediate, and final covers. Humid continental cool summers were 135, 40, and 26 g CH4/m2/day for the working face, intermediate, and final covers. Mediterranean climates were examined for intermediate and final covers only and found to be 11 and 6 g CH4/m2/day, respectively, whereas semiarid climates showed 85, 11, 3.7, and 2.7 g CH4/m2/day for working face, temporary, intermediate, and final covers. A closed, synthetically capped landfill covered with soil and vegetation with a gas collection system in a humid continental warm summer climate gave mostly background methane readings and average emission rates of only 0.09 g CH4/m2/day flux when measurable.     

Boundary Layer Emission Monitoring

A new methodology is described for determining the atmospheric emission rate of pollutants from large heterogeneous area sources, such as hazardous waste sites. The procedure hinges upon measuring average pollutant concentrations, at three or more different elevations, while traversing the plume downwind of the area source. A helium-filled tethersonde balloon is used to elevate the sampling lines to their appropriate height. During plume traversing the sampling rate is adjusted to be proportional to the sine of the angle between the wind vector and the direction of the traverse path. The average concentrations are corrected for any upwind, background concentration and then used to derive an average vertical concentration profile. This profile Is numerically integrated, with the wind velocity profile, over the pollutant boundary layer to yield the area source emission rate. The methodology was tested on several large industrial effluent lagoons and proved to be easy to use, robust, and precise.     

Meteorological Considerations in Air Quality Planning

Results from dispersion models which are routinely used for regulatory purposes do not reflect the uncertainties which are Inherent In the input data. To remedy this, a formula for propagating measurement uncertainties of emission rate, wind speed, wind direction, horizontal dispersion parameter, vertical dispersion parameter, effective emission height, and mixing depth is derived for EPA’s Industrial Source Complex Short Term (ISCST) Gaussian dispersion model for the simple case of a single stack-type source and nonbuoyant plume. Values for the uncertainties of the input variables are chosen and used to calculate ambient concentration uncertainties. These calculated uncertainties are compared with the standard deviation of ambient concentrations calculated from 2500 input data sets for each of four stability classes and three downwind distances, which were randomly altered to simulate the effects of measurement uncertainty. The calculated uncertainties do not differ significantly from the standard deviations of the randomized calculations for input data uncertainties as high as 30 percent and Stability Classes A-C. The calculated uncertainties overestimate the actual uncertainty of model calculations for input data uncertainties greater than 20 percent for Stability Class D.     

Innovative approach for estimating fugitive gaseous fluxes using computed tomography and remote optical sensing techniques.

This paper presents a new approach to quantify emissions from fugitive gaseous air pollution sources. The authors combine Computed Tomography (CT) with Path-Integrated Optical Remote Sensing (PI-ORS) concentration data in a new field beam geometry. Path-integrated concentrations are sampled in a vertical plane downwind from the source along several radial beam paths. An innovative CT technique, which applies the Smooth Basis Function Minimization method to the beam data in conjunction with measured wind data, is used to estimate the total flux from the fugitive source. The authors conducted a synthetic data study to evaluate the proposed methodology under different meteorological conditions, beam geometry configurations, and simulated measurement errors. The measurement errors were simulated based on data collected with an Open-Path Fourier Transform Infra-Red system. This approach was found to be robust for the simulated errors and for a wide range of fluctuating wind directions. In the very sparse beam geometry examined (eight beam paths), successful emission rates were retrieved over a 70 degrees range of wind directions under extremely large measurement error conditions.     

Innovative Approach for Estimating Fugitive Gaseous Fluxes Using Computed Tomography and Remote Optical Sensing Techniques

ABSTRACT

This paper presents a new approach to quantify emissions from fugitive gaseous air pollution sources. The authors combine Computed Tomography (CT) with Path-Integrated Optical Remote Sensing (PI-ORS) concentration data in a new field beam geometry. Path-integrated concentrations are sampled in a vertical plane downwind from the source along several radial beam paths. An innovative CT technique, which applies the Smooth Basis Function Minimization method to the beam data in conjunction with measured wind data, is used to estimate the total flux from the fugitive source. The authors conducted a synthetic data study to evaluate the proposed methodology under different meteorological conditions, beam geometry configurations, and simulated measurement errors. The measurement errors were simulated based on data collected with an Open-Path Fourier Transform Infra-Red system. This approach was found to be robust for the simulated errors and for a wide range of fluctuating wind directions. In the very sparse beam geometry examined (eight beam paths), successful emission rates were retrieved over a 70° range of wind directions under extremely large measurement error conditions.     

Theoretical study of the impact of particulate matter gravitational settling on ambient coarse particulate matter monitoring for agricultural emissions

The particle size distributions (PSDs) of particulate matter (PM) in the downwind plume from simulated sources of a cotton gin were analyzed to determine the impact of PM settling on PM monitoring. The PSD of PM in a plume varies as a function of gravitational settling. Gravitational settling has a greater impact on the downwind PSD from sources with PSDs having larger mass median diameters (MMDs). The change in PSD is a function of the source PSD of emitted PM, wind speed, and downwind distance. Both MMD and geometric standard deviation (GSD) in the downwind plume decrease with an increase in downwind distance and source MMD. The larger the source MMD, the greater the change in the downwind MMD and GSD. Also, the greater the distance from the source to the sampler, the greater the change in the downwind MMD and GSD. Variations of the PSD in the downwind plume significantly impact PM10 sampling errors associated with the U.S. Environmental Protection Agency (EPA) PM10 samplers. For the emission sources with MMD > 10 microm, the PM10 oversampling rate increases with an increase in downwind distance caused by the decrease of GSD of the PSD in the downwind plume. Gravitational settling of particles does not help reduce the oversampling problems associated with the EPA PM10 sampler. Furthermore, oversampling rates decrease with an increase of the wind speed.     

Performance Audits of EPA Protocol Gases and Inspection and Maintenance Calibration Gases

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the “with measures” scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance.

Implications:?A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.     

Estimating Gas Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

Abstract

Manure storage tanks and animals in barns are important agricultural sources of methane. To examine the possibility of using an inverse dispersion technique based on a backward Lagrangian Stochastic (bLS) model to quantify methane (CH₄) emissions from multiple on-farm sources, a series of tests were carried out with four possible source configurations and three controlled area sources. The simulated configurations were: (C1) three spatially separate ground-level sources, (C2) three spatially separate sources with wind-flow disturbance, (C3) three adjacent ground-level sources to simulate a group of adjacent sources with different emission rates, and (C4) a configuration with a ground level and two elevated sources. For multiple ground-level sources without flow obstructions (C1 and C3), we can use the condition number (k, the ratio of the uncertainty in the calculated emission rate to the uncertainty in the predicted ratio of concentration to emission rate) to evaluate the applicability of this inverse dispersion technique and a preliminary threshold of k < 10 is recommended. For multiple sources with wind disturbance (C2) or an even more complex configuration including ground level and elevated sources (C4), a low k is not sufficient to provide reasonable discrete and total emission rates. The effect of flow obstructions can be neglected as long as the distance between the source and the measurement location is greater than approximately 10 times the height of the flow obstructions. This study shows that the bLS model has the potential to provide accurate discrete emission rates from multiple on-farm emissions of gases provided that certain conditions are met.     

Fine particulate matter emissions inventories: comparisons of emissions estimates with observations from recent field programs

Emissions inventories of fine particulate matter (PM2.5) were compared with estimates of emissions based on data emerging from U.S. Environment Protection Agency Particulate Matter Supersites and other field programs. Six source categories for PM2.5 emissions were reviewed: on-road mobile sources, nonroad mobile sources, cooking, biomass combustion, fugitive dust, and stationary sources. Ammonia emissions from all of the source categories were also examined. Regional emissions inventories of PM in the exhaust from on-road and nonroad sources were generally consistent with ambient observations, though uncertainties in some emission factors were twice as large as the emission factors. In contrast, emissions inventories of road dust were up to an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although comprehensive nationwide emissions inventories of PM2.5 from cooking sources and biomass burning are not yet available, observational data in urban areas suggest that cooking sources account for approximately 5-20% of total primary emissions (excluding dust), and biomass burning sources are highly dependent on region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for PM2.s are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.     

Application of a combined measurement and modeling method to quantify windblown dust emissions from the exposed playa at Mono Lake, California

Particulate matter < or =10 microm (PM10) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM10 monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km2 area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM10 concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM10 concentrations to back-calculate the ratio of vertical PM10 flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K(f)) were found to change seasonally, ranging from 1.3 x 10(-5) to 5.1 x 10(-5) for sand flux measured at 15 cm above the surface (q15). Hourly PM10 emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F = K(f) x q15). The maximum hourly PM10 emission rate from the study area was 76 g/m2 x hr (10-m wind speed = 23.5 m/sec). Maximum daily PM10 emissions were estimated at 450 g/m2 x day, and annual emissions at 1095 g/m2 x yr. Hourly PM10 emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM10 ranging from 16 to over 60,000 microg/m3 (slope = 0.89, R2 = 0.77).     

Remote monitoring of air pollutant emissions from point sources by a mobile lidar/sodar system

This paper describes remote monitoring of air pollutant emissions by a mobile lidar (light detection and ranging)/ sodar (sound detection and ranging) system. First, measurements are carried out in the flue gas plume of a public power plant. The investigations focus mainly on quantifying SO2 emissions, but the uncertainties of such measurements are also emphasized. Furthermore, an example providing valuable data sets for the development and validation of plume dispersion models is outlined with measurements of the dilution of SO2 along the plume axis. Series of repeated determinations of SO2 emissions show a large variation in the obtained flux values, with moderate margins of error. Incomplete recording of the plume within the individual lidar scans, induced by strong looping movements of the flue gas plume, predominantly causes the variations of flux values. Therefore, the highest flux values determined are considered to be the most exact. This is verified by a comparison of measured fluxes with in situ measurements made by the plant operators. The results further indicate that lidar measurements illustrate the location and dimension of aerosol plumes better than the location and dimension of the plumes of gaseous compounds. The wind direction affecting the plume at any moment can be determined faster by lidar than by sodar because the latter requires much longer time intervals of signal averaging. Measurements show higher concentrations of SO2 compared with results from a Gaussian plume model for periods of less than 5 min after dispersion. The findings emphasize the suitability of remote sensing for detecting emissions and for investigating the propagation and dilution of air pollutant plumes.     

Preparation of mercury emissions inventory for eastern North America

Point and area inventories of anthropogenic mercury emissions documented by US and Canadian environmental agencies have been aggregated into a single archive for analysis and air pollution modeling work. For 5341 point sources and 1634 aggregated area sources, mercury emissions are apportioned among elemental gaseous [Hg(0)], reactive gaseous[Hg(II)], and particulate [Hg(p)] emissions using speciation factors derived from available monitoring measurements. According to this inventory, 4.82 x 10(5) mol of mercury were emitted in calendar year 1996 in the latitude range 24-51 degrees north, and longitude range 64-91 degrees west, which covers most of North America east of the Mississippi River. Using speciation factors consistent with past emission source studies, we find the relative emission proportions among Hg(0):Hg(II):Hg(p) species are 47:35:18. Maps of the various mercury species' emissions patterns are presented. Gridded emission patterns show local mercury emission extremes associated with individual cement production and municipal incineration facilities, and in contrast to past inventories, population centers do not stand out. Considerable uncertainties are still present in estimating emissions from large point sources, as are methods of apportioning emissions among various mercury species.     

Evaluating the performance of the horizontal radial plume mapping technique for locating multiple plumes

This paper evaluated the feasibility of using the horizontal radial plume mapping (HRPM) technique to locate multiple emission sources via computational simulation. Seventy-two test maps, each having two Gaussian distributions, were generated in a two-dimensional domain. The HRPM technique with the non-negative least square (NNLS) algorithm was then applied to reconstruct the plumes, assuming a nine-beam scanning beam geometry. The NNLS algorithm successfully reconstructed the source locations of 68 of the 72 test maps. However, when one of the plumes was near the origin, the NNLS did not always identify the peak locations correctly. Furthermore, when the two plumes were spaced closely, the NNLS tended to reconstruct a wide plume covering both plumes instead of separating them due to the resolution limitation of the current nine-beam geometry. In the sensitivity analysis, five sets of random error (1%, 5%, 10%, 20%, and 30%) were added in the path-integrated concentration (PIC) from the 72 test maps, and thus, an additional 360 reconstructions were implemented. Robust results were obtained when the noise added was less than 20%. The results generally support the implementation of the NNLS algorithm in the HRPM technique as described in the U.S. Environmental Agency (EPA) Other Test Method 10 (OTM-10).

Implications: The methodology evaluated in this paper provides near-real-time estimates about the locations of multiple emission sources. The involved optical remote sensing instruments can monitor large spatial areas (e.g., landfills) in a cost-effective way.     

Numerical simulation of air pollution in urban areas: model performance

The numerical model developed in the first part of this investigation is applied to assess the behavior of sulfur dioxide and sulfate concentration distributions in an urban area using the St Louis Regional Air Pollution Study (RAPS) data. Statistical techniques chosen to determine the accuracy and uncertainty associated with the numerical model results include paired analysis and resampling analysis. The results of the numerical model are also compared with those of RAM, a Gaussian plume model. Finally, the behavior of point and area emission sources in an urban area is assessed to provide an insight into the complex interrelationships between the emissions and meteorological conditions which determine the distribution of ground level concentrations.     

Temporal variations of source impacts at the receptor,as derived from air particulate monitoring data in Ho Chi Minh City,Vietnam

Varimax rotation factor analysis was applied to monthly concentrations of elements in total suspended air particulate (TSP) matter in Ho Chi Minh City collected from December 1992 to November 1996, covering four dry/rainy seasons. Six pollution source types were revealed. Resuspended soil/road dust accounts for 74% of the TSP mass loading. Motor vehicles and a source which emits particulates containing arsenic account for 10% and 9%, respectively. There are three minor sources, namely, cement dust from the nearby construction site, road dust of local traffic origin and burning emissions. The contributions from these source were estimated with high uncertainties. The interpretation of sources was corroborated by studying source profiles and temporal variations of source contributions. The monthly variations of source contributions at the receptor were modelled by using source apportionment techniques. From the variation patterns, emission scenarios for burning, construction and motor vehicle sources were reproduced. Source contributions also exhibit seasonal variability induced by changes of meteorological conditions. No seasonal change was found for the As-containing particulates, suggesting a speculation on their origin as coal fly ash emitting from any local coal burning source.     

Fine Particulate Matter Emissions Inventories: Comparisons of Emissions Estimates with Observations from Recent Field Programs

Abstract

Emissions inventories of fine particulate matter (PM_2.5) were compared with estimates of emissions based on data emerging from U.S. Environment Protection Agency Particulate Matter Supersites and other field programs. Six source categories for PM_2.5 emissions were reviewed: on-road mobile sources, nonroad mobile sources, cooking, biomass combustion, fugitive dust, and stationary sources. Ammonia emissions from all of the source categories were also examined. Regional emissions inventories of PM in the exhaust from on-road and nonroad sources were generally consistent with ambient observations, though uncertainties in some emission factors were twice as large as the emission factors. In contrast, emissions inventories of road dust were up to an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although comprehensive nationwide emissions inventories of PM_2.5 from cooking sources and biomass burning are not yet available, observational data in urban areas suggest that cooking sources account for approximately 5–20% of total primary emissions (excluding dust), and biomass burning sources are highly dependent on region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for PM_2.5 are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM_2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.