Localizing gaseous fugitive emission sources by combining real-time optical remote sensing and wind data

This paper presents a new approach to localize point emissions from ground-level fugitive gaseous air pollution sources. We estimate the crosswind plume's ground-level peak location downwind from the source by combining smooth basis functions minimization (SBFM) with pathintegrated optical remote sensing concentration data acquired along the crosswind direction in alternating beam path lengths. Peak location estimates, in conjunction with real-time measured wind direction data, are used to reconstruct the fugitive source location. We conducted a synthetic data study to evaluate the proposed peak location SBFM reconstruction. Furthermore, the methodology was validated with open-path Fourier transform infrared concentration data collected with wind direction data downwind from a controlled point source. This approach was found to provide reasonable estimates of point source location. The field study reconstructed source location was within several meters of the real source location.     

The Odor Impact Model

Abstract

This paper presents the simulation and field evaluation results of two approaches to localize pollutant emission sources with open-path Fourier transform infrared (OPFTIR) spectroscopy. The first approach combined the plume’s peak location information reconstructed from the Smooth Basis Function Minimization (SBFM) algorithm and the wind direction data to calculate source projection lines. In the second approach, the plume’s peak location was determined with the Monte Carlo methodology by randomly sampling within the beam segment having the largest path-integrated concentration. We first conducted a series of simulation studies to investigate the sensitivity of using different basis functions in the SBFM algorithm. It was found that fitting with the beta and Weibull basis functions generally gave better estimates of the peak locations than with the normal basis function when the plumes were mainly within the OP-FTIR’s monitoring line. However, for plumes that were symmetric to the peak position or spread over the OP-FTIR, fitting with the normal basis function gave better performance. In the field experiment, two tracer gases were released simultaneously from two locations and the OP-FTIR collected data downwind from the sources with a maximum beam path length of 97 m. For the first approach, the release locations were within the 0.25- to 0.5-probability area only after the uncertainty of the peak locations was included in the calculation process. The second approach was easy to implement and still performed as satisfactorily as the first approach. The distances from the sources to the best-fit lines (i.e., the regression lines) of the estimated locations were smaller than 10 m.     

Developing and evaluating techniques for localizing pollutant emission sources with open-path Fourier transform infrared measurements and wind data

This paper presents the simulation and field evaluation results of two approaches to localize pollutant emission sources with open-path Fourier transform infrared (OP-FTIR) spectroscopy. The first approach combined the plume's peak location information reconstructed from the Smooth Basis Function Minimization (SBFM) algorithm and the wind direction data to calculate source projection lines. In the second approach, the plume's peak location was determined with the Monte Carlo methodology by randomly sampling within the beam segment having the largest path-integrated concentration. We first conducted a series of simulation studies to investigate the sensitivity of using different basis functions in the SBFM algorithm. It was found that fitting with the beta and Weibull basis functions generally gave better estimates of the peak locations than with the normal basis function when the plumes were mainly within the OP-FTIR's monitoring line. However, for plumes that were symmetric to the peak position or spread over the OP-FTIR, fitting with the normal basis function gave better performance. In the field experiment, two tracer gases were released simultaneously from two locations and the OP-FTIR collected data downwind from the sources with a maximum beam path length of 97 m. For the first approach, the release locations were within the 0.25- to 0.5-probability area only after the uncertainty of the peak locations was included in the calculation process. The second approach was easy to implement and still performed as satisfactorily as the first approach. The distances from the sources to the best-fit lines (i.e., the regression lines) of the estimated locations were smaller than 10 m.     

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.     

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

Abstract

Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner.     

The Influence of Annual Meteorological Variations On Regional Air Pollution Modeling: A Case Study of Allegheny County,Pennsylvania

The influence of year-to-year meteorological variations on the prediction of annual average ground-level pollutant concentrations has been examined via case studies of Allegheny County, Pa. Twenty-two stability wind roses representing different averaging intervals of from one to seven years were employed in the Air Quality Display Model to predict annual average SO₂ concentrations in two multiple source sub-basins, and from two single point sources representing industrial and utility boiler stacks. Effects of annual meteorological variations were manifested by changes in the magnitude of peak concentrations, the location of peak concentrations, and the geographic distribution of pollutants. For fixed rates of emission, the peak annual average SO₂ ground-level concentration varied by an average of up to 33% of highest values for point sources and 17% for sub-basin complexes. In both cases, there was relatively little change in the location of peak concentration, though occasional directional shifts were noted. In contrast, marked variations were noted in the geographic area exposed to annual average concentrations in excess of several selected values. To aid in regional planning, several methods were formulated which considerably reduced the uncertainty in predicting peak annual concentration for varying degrees of historical data on regional stability wind rose. These methods are-especially applicable to analysis of control strategies directed at attaining annual ambient air quality standards which nominally must never be exceeded.     

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.     

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.     

Atmospheric Tracer Techniques and Gas Transport in the Primary Aluminum Industry

The results of 35 Individual SF₆ tracer tests conducted in Norway during 1978 demonstrate the applicability of tracer techniques to the study of a wide variety of pollutant transport problems found in the primary aluminum industry. Tracer methods were employed to determine the efficiency of the pollutant control system over a single reduction cell under a variety of operating conditions. Two tests conducted during normal operation gave efficiencies equal to 100 ±19% and 79 ± 12%, while a test performed during the occurrence of an anode effect yielded an efficiency equal to 66 ± 22%.

Tracer investigations of flow in the wake of a smelter hall indicated that between 1 % and 11 % of secondary, roof-top emissions can become entrained in the recirculation cavity and reenter the hall through the ventilation fresh air supply. These reentry rates were observed for release heights as high as 8 m above the existing roof exhaust duct. Tracer dispersion data collected within 20 building heights of the smelter agreed very well with extrapolations of McEIroy- Pooler dispersion curves for an urban area. Dispersion curves determined from a previous wind tunnel study of flow downwind of an isolated building underestimated dispersion downwind of the vs.melter complex.

The total fluoride mass flow rate measured downwind of a smelter during wet, foggy conditions indicated that wet removal rates of fluorides are in the range 3.2 × 10^?4/s to 6.4 × 10^?4/s. Simulation of the source with several tracer point releases and simultaneous measurement of fluoride and tracer ground-level concentrations downwind of the smelter eliminated the need for measurements of vertical profiles of wind speed and fluoride concentration during the experiment.     

An Inversion Algorithm for Determining Area-Source Emissions from Downwind Concentration Measurements

Measuring emissions from nonuniform area sources, such as waste repository sites, has been a difficult problem. A simple but reliable method is not available. An objective method of inverting downwind concentration measurements, utilizing an assumed form of atmospheric dispersion to reconstruct total emission rate and distribution, is described in this study. The Gaussian dispersion model is compared to a more realistic model based on K-theory and similarity expressions. A sensitivity analysis is presented indicating the atmospheric conditions under which a successful application of the method could be anticipated. Field releases of sulfur hexaf luoride (SF₆) from a simulated area source in flat terrain were conducted to check the method,ability to reconstruct source distribution, and total emission rate. The sensitivity analysis and the field study confirm that a few ground-level concentration measurements and a simple determination of the atmospheric dispersion characteristics are sufficient, under neutral to stable conditions, to obtain the total emission rate accurately. Reconstruction of the spatial pattern of the source is possible by utilizing concentration information from samplers located on two separate ground-level receptor lines, if a shift in the wind direction occurs and if it can be assumed that the total emission rate is time invariant. A method of cross-checking the accuracy of the reconstruction, using a simultaneous tracer release, is presented.     

Acid aerosols in the Pittsburgh Metropolitan area

This article presents data on ambient concentrations of selected acidic aerosols at four existing monitoring sites in the Pittsburgh PA metropolitan area. The data were collected by staff of the Allegheny County Health Department, Division of Air Quality during the summer and fall of 1993. The sampling protocol was focused on obtaining 24 h-average ammonia, ammonium, acidic sulfates, and particle strong acids data on a 2 to 3 day cycle. The data were obtained using Harvard University School of Public Health's “Short-HEADS” annular denuder sampling train. The Pittsburgh area is of interest because it is downwind of a major regional source of sulfur and nitrogen emissions from coal-burning power plants: the Ohio River Valley. The data presented here indicate that ground-level concentrations of acidic aerosols in Pittsburgh are highly correlated spatially and that many pollutants are higher on days when ground-level wind direction vectors indicate that wind is coming from the southwest rather than from the Pittsburgh source area itself. The monitoring site that is most upwind of the Pittsburgh source area – South Fayette – has particle strong acid levels about twice those of sites closer in to the Pittsburgh central business district.     

Simulation of large particle transport near the surface under stable conditions: comparison with the Hanford tracer experiments

A plume model is presented describing the downwind transport of large particles (1–100 μm) under stable conditions. The model includes both vertical variations in wind speed and turbulence intensity as well as an algorithm for particle deposition at the surface. Model predictions compare favorably with the Hanford single and dual tracer experiments of crosswind integrated concentration (for particles: relative bias=−0.02 and 0.16, normalized mean square error=0.61 and 0.14, for the single and dual tracer experiments, respectively), whereas the US EPA's fugitive dust model consistently overestimates the observed concentrations at downwind distances beyond several hundred meters (for particles: relative bias=0.31 and 2.26, mean square error=0.42 and 1.71, respectively). For either plume model, the measured ratio of particle to gas concentration is consistently overestimated when using the deposition velocity algorithm of Sehmel and Hodgson (1978. DOE Report PNL-SA-6721, Pacific Northwest Laboratories, Richland, WA). In contrast, these same ratios are predicted with relatively little bias when using the algorithm of Kim et al. (2000. Atmospheric Environment 34 (15), 2387–2397).     

Real–Time Measurement of Outdoor Tobacco Smoke Particles

Abstract

The current lack of empirical data on outdoor tobacco smoke (OTS) levels impedes OTS exposure and risk assessments. We sought to measure peak and time-averaged OTS concentrations in common outdoor settings near smokers and to explore the determinants of time-varying OTS levels, including the effects of source proximity and wind. Using five types of real-time airborne particle monitoring devices, we obtained more than 8000 min worth of continuous monitoring data, during which there were measurable OTS levels. Measurement intervals ranged from 2 sec to 1 min for the different instruments. We monitored OTS levels during 15 on-site visits to 10 outdoor public places where active cigar and cigarette smokers were present, including parks, sidewalk cafés, and restaurant and pub patios. For three of the visits and during 4 additional days of monitoring outdoors and indoors at a private residence, we controlled smoking activity at precise distances from monitored positions. The overall average OTS respirable particle concentration for the surveys of public places during smoking was approximately 30 μg m^?3. OTS exhibited sharp spikes in particle mass concentration during smoking that sometimes exceeded 1000 μg m^?3 at distances within 0.5 m of the source. Some average concentrations over the duration of a cigarette and within 0.5 m exceeded 200 μg m^?3, with some average downwind levels exceeding 500 μg m^?3. OTS levels in a constant upwind direction from an active cigarette source were nearly zero. OTS levels also approached zero at distances greater than approximately 2 m from a single cigarette. During periods of active smoking, peak and average OTS levels near smokers rivaled indoor tobacco smoke concentrations. However, OTS levels dropped almost instantly after smoking activity ceased. Based on our results, it is possible for OTS to present a nuisance or hazard under certain conditions of wind and smoker proximity.     

Maximum Ground-Level Concentrations with Downwash: The Urban Stability Mode

Abstract

Gaussian model-based equations for critical downwind distance, wind speed, and plume height that result in maximum ground-level concentrations (MGLC) under downwash conditions for the rural stability mode were presented in a previous paper. This paper presents general equations for the critical downwind distance xc for the urban stability mode. Specific examples are presented for Schulman-Scire and Huber-Snyder downwash treatments for building-enhanced and regular sigmas.     

The Limitations of Air Quality Standards

This paper describes a diffusion model designed to permit calculation of seasonal average concentrations of an air pollutant, in particular, sulfur dioxide. The calculations can encompass multiple sources and multiple receptors. For each receptor location the model sums the effect of all sources over a wide range of meteorological conditions. Input data include source pollutant emissions, source configuration and location, receptor location, and meteorological data expressed as a joint frequency distribution of wind direction, wind speed, stability. To determine the model’s accuracy, concentration estimates for St. Louis, Mo., are compared with measured SO₂ concentrations. The overall correlation with observed data is satisfactory. A computer program to handle the numerous calculations was written in Fortran IV language for use on an IBM 1130 computer.     

Lidar-assisted measurement of PM10 emissions from agricultural tilling in California's San Joaquin Valley – Part I: lidar

Vertical profiling with point samplers is an accepted method for quantifying the fluxes of PM₁₀ from non-point fugitive dust sources, but is limited by uncertainty in estimates of the actual height of the dust plume, especially for plumes that exceed the highest sampling height. Agricultural land preparation operations in the San Joaquin Valley were monitored using upwind–downwind vertical PM₁₀ profiles and data collected during the first successful experiment to include light detection and ranging (lidar), in 1998, were analyzed to provide modeling criteria for the 1996 and 1997 data. A series of six comprehensive PM₁₀ tests with concurrent lidar data was examined to: (a) develop a framework for analyzing upwind–downwind point PM₁₀ concentration profiles of land preparation operations (disking, listing, root cutting, and ripping) and (b) identify conditions under which the field sampling strategies affect the reproducibility of PM₁₀ concentration measurements. Lidar data were used to verify that the plume heights and shapes extrapolated from the point sampler vertical profiles adequately described the plumes. The shortcomings of the vertical profiling technique and lidar methods are discussed in the light of developing efficient robust methods for accurate PM₁₀ emissions quantification from complex non-point sources.     

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.     

Modeling Emissions from Elevated Cylindrical Bins

Abstract

This paper demonstrates how wind tunnel modeling data that accurately describe plume characteristics near an unconventional emission source can be used to improve the near-field downwind plume profiles predicted by conventional air pollution dispersion models. The study considers a vertical, cylindrical-shaped, elevated bin similar to large product storage bins that can be found at many industrial plant sites. Two dispersion models are considered: the U.S. Environmental Protection Agency's ISC2(ST) model and the Ontario Ministry of the Environment and Energy's GAS model. The wind tunnel study showed that plume behavior was contrary to what was predicted using conventional dispersion models such as ISC2(ST) and GAS and default values of input parameters. The wind tunnel data were used to develop a protocol for correcting the dispersion models inputs, resulting in a substantial improvement in the accuracy of the dispersion estimates.     

Using pollution roses to assess sulfur dioxide impacts in a township downwind of a petrochemical complex

This study used pollution roses to assess sulfur dioxide (SO₂) pollution in a township downwind of a large petrochemical complex based on data collected from a single air quality monitoring station. The pollution roses summarized hourly SO₂ concentrations at the Taishi air quality monitoring station, located approximately 7.8–13.0 km south of the No. 6 Naphtha Cracking Complex in Taiwan, according to 36 sectors of wind direction during the preoperational period (1995–1999) and two postoperational periods (2000–2004 and 2005–2009). The 99th percentile of hourly SO₂ concentrations 350? downwind from the complex increased from 28.9 ppb in the preoperational period to 86.2–324.2 ppb in the two postoperational periods. Downwind SO₂ concentrations were particularly high during 2005–2009 at wind speeds of 6–8 m/sec. Hourly SO₂ levels exceeded the U.S. Environmental Protection Agency (EPA) health-based standard of 75 ppb only in the postoperational periods, with 65 exceedances from 0–10? and 330–350? downwind directions during 2001–2009. This study concluded that pollution roses based on a single monitoring station can be used to investigate source contributions to air pollution surrounding industrial complexes, and that it is useful to combine such directional methods with analyses of how pollution varies between different wind speeds, times of day, and periods of industrial development.

Implications: The pollution roses summarize SO₂ concentrations by wind direction and to investigate source contribution to air quality. Percentile statistics can catch pollution episodes occurring in a very short time at specific wind directions and speeds. The downwind areas have already exceeded regulated 1-hr SO₂ standard since the operation of the complex.     

The Change of Concentration Standard Deviations with Distance

Calculations of pollutant concentrations at various downwind and crosswind distances from an elevated point source are frequently based on diffusion models and parameter values contained in Turner’s Workbook of Atmospheric Dispersion Estimates.1 These calculations can be made much more speedily on a computer or on a currentgeneration, programmable desk calculator, if the values of the horizontal and vertical standard deviations of plume concentrations distributions are stated explicitly as a mathematical function of downwind distance. The alternative is to read the values from Turner’s Figures 3-2 and 3-3 and input them into the calculation for each individual value of downwind distance.