Emission rates of benzene and ammonia area sources determined by spectroscopic remote measurements and inverse dispersion modeling

1999 ◽  
Author(s):  
Klaus Schaefer ◽  
Stefan M. Emeis ◽  
Martina Stockhause ◽  
Achim Sedlmaier ◽  
Herbert Hoffmann ◽  
...  
Keyword(s):  
Dispersion Modeling ◽  
Emission Rates ◽  
Inverse Dispersion ◽  
Remote Measurements ◽  
Inverse Dispersion Modeling

Inverse AERMOD and SCIPUFF Dispersion Modeling for Farm-Level PM10 Emission Rate Assessment

2021 ◽  
Vol 64 (3) ◽  
pp. 801-817
Author(s):  
Bin Cheng ◽  
Aditya Padavagod Shiv Kumar ◽  
Lingjuan Wang-Li
Keyword(s):  
Wind Speed ◽  
Egg Production ◽  
Single Point ◽  
Dispersion Modeling ◽  
Emission Rates ◽  
Farm Level ◽  
Area Source ◽  
Inverse Dispersion ◽  
Pm10 Emission ◽  
Inverse Dispersion Modeling

HighlightsAERMOD and SCIPUFF were employed to back-calculate farm-level PM10 emission rates based on inverse modeling.Both AERMOD and SCIPUFF did not capture the diurnal and seasonal variations of farm-level PM10 emission rates.AERMOD modeling results were affected by wind speed, with higher wind speed leading to higher emission rates.Higher numbers of receptors and PM10 measurements with greater time resolution may be recommended in the future.Abstract. Air pollutant emissions from animal feeding operations (AFOs) have become a serious concern for public health and ambient air quality. Particulate matter with aerodynamic equivalent diameter less than or equal to 10 µm (PM10) is one of the major air pollutants emitted from AFOs. To assess the impacts of PM10 emissions from AFOs, knowledge about farm-level PM10 emission rates is needed but is challenging to obtain through field measurements. The inverse dispersion modeling approach provides an alternative way to estimate farm-level PM10 emission rates. In this study, two dispersion models, AERMOD and SCIPUFF, were employed to back-calculate farm-level PM10 emission rates based on hourly PM10 concentration measurements at four downwind locations in the vicinity of a commercial egg production farm in the southeast U.S. Onsite meteorological data were simultaneously recorded using a 10 m weather tower to facilitate the dispersion modeling. The modeling results were compared with PM10 emission measurements from two layer houses on the farm. Single-area source, double-area source, and double-volume source were used in AERMOD, while only single-point source was used in SCIPUFF. The inverse modeling results indicated that both SCIPUFF and AERMOD did not capture the diurnal and seasonal variations of the farm-level PM10 emission rates. In addition, the AERMOD modeling results were affected by wind speed, and higher emission rates may be predicted at higher wind speeds. The single-point source for SCIPUFF, the plume rise simplification for AERMOD, and insufficient concentration measurement resolution in response to temporal changes in wind direction may have added uncertainties to the modeling results. The results of this study suggest that more receptors covering more representative downwind locations should be considered in future modeling for farm-level emissions assessment. Moreover, ambient data collection with greater time resolution (e.g., less than one hour) is recommended to capture diurnal and seasonal patterns more rigorously. Only in this way can researchers achieve a better understanding of the effectiveness of inverse dispersion modeling for estimation of pollutant emission rates. Keywords: AERMOD, Animal feeding operations, Egg production, Farm-level emission rate, Inverse dispersion modeling, PM10, SCIPUFF.

Inverse Dispersion Modeling of Bioaerosol Emissions from Manure Application Sites

2013 ◽  
Author(s):  
Michael A Jahne ◽  
Shane W Rogers ◽  
Kelsey Lopez ◽  
Seungo Kim ◽  
Stefan J Grimberg ◽  
...  
Keyword(s):  
Dispersion Modeling ◽  
Manure Application ◽  
Inverse Dispersion ◽  
Inverse Dispersion Modeling

scholarly journals Towards robust on-site ammonia emission measuring techniques based on inverse dispersion modeling

2021 ◽  
Vol 307 ◽  
pp. 108517
Author(s):  
Eva Herrero ◽  
Alberto Sanz-Cobena ◽  
Viviana Guido ◽  
Mónica Guillén ◽  
Arturo Dauden ◽  
...  
Keyword(s):  
Ammonia Emission ◽  
Dispersion Modeling ◽  
Inverse Dispersion ◽  
Measuring Techniques ◽  
Inverse Dispersion Modeling

Use of Source Term and Air Dispersion Modeling in Planning Demolition of Highly Alpha-Contaminated Buildings

2011 ◽  
Author(s):  
James G. Droppo ◽  
Bruce A. Napier ◽  
Jeremy P. Rishel ◽  
Richard W. Bloom
Keyword(s):  
Source Term ◽  
Dispersion Model ◽  
Dispersion Modeling ◽  
Nuclear Materials ◽  
Emission Rates ◽  
The Public ◽  
Air Dispersion Modeling ◽  
Air Dispersion ◽  
Air Dispersion Model ◽  
War Production

The current cleanup of structures related to cold-war production of nuclear materials includes the need to demolish a number of highly alpha-contaminated structures. The process of planning for the demolition of such structures includes unique challenges related to ensuring the protection of both workers and the public. Pre-demolition modeling analyses were conducted to evaluate potential exposures resulting from the proposed demolition of a number of these structures. Estimated emission rates of transuranic materials during demolition are used as input to an air-dispersion model. The climatological frequencies of occurrence of peak air and surface exposures at locations of interest are estimated based on years of hourly meteorological records. The modeling results indicate that downwind deposition is the main operational limitation for demolition of a highly alpha-contaminated building. The pre-demolition modeling directed the need for better contamination characterization and/or different demolition methods—and in the end, provided a basis for proceeding with the planned demolition activities. Post-demolition modeling was also conducted for several contaminated structures, based on the actual demolition schedule and conditions. Comparisons of modeled and monitoring results are shown. Recent monitoring data from the demolition of a UO3 plant shows increments in concentrations that were previously identified in the pre-demolition modeling predictions; these comparisons confirm the validity and value of the pre-demolition source-term and air dispersion computations for planning demolition activities for other buildings with high levels of radioactive contamination.

scholarly journals Comparison of Hydrogen Sulfide Concentrations and Odor Annoyance Frequency Predictions Downwind from Livestock Facilities

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 249
Author(s):  
Suraiya Akter ◽  
Erin L. Cortus
Keyword(s):  
Hydrogen Sulfide ◽  
Single Point ◽  
Weighted Average ◽  
Weather Conditions ◽  
Time Of Day ◽  
Dispersion Modeling ◽  
Emission Rates ◽  
Weather Patterns ◽  
Odor Emission ◽  
Odor Annoyance

Estimating and measuring the occurrence of a sensation, odor, around livestock facilities is challenging. This research compares the estimated frequency of odor nuisance with measured hydrogen sulfide (H2S) concentrations at various distances around a swine and a dairy operation, and discusses the results based on time of day, weather conditions, distance, and topography. The estimated odor annoyance-free and odor annoyance frequencies were based on a publically available calculator of odor impact derived from average odor emission rates, historical, and regional weather patterns, and dispersion modeling. Continuous monitoring of H2S was by single point monitors (SPM) at locations around the operations. Time-weighted average H2S concentrations were less than 10 ppb for odor annoyance-free frequencies, and less than 10 to at least 73 ppb for odor-annoyance frequencies. Verifying a proxy odor indicator can help update odor annoyance models and respond to site-specific concerns for existing facilities.

scholarly journals Inverse Dispersion for an Unknown Number of Sources: Model Selection and Uncertainty Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Eugene Yee
Keyword(s):  
Recursive Algorithm ◽  
Atmospheric Dispersion ◽  
Source Model ◽  
Bayesian Probability ◽  
Dispersion Modeling ◽  
Concentration Data ◽  
Measured Concentration ◽  
Inverse Dispersion ◽  
Unknown Number ◽  
Localized Sources

A simple recursive method is presented for performing the inverse dispersion modeling of an unknown number of (localized) sources, given a finite number of noisy concentration data acquired by an array of detectors. Bayesian probability theory is used to address the problem of selecting the source model which is most plausible in view of the given concentration dataset and all the available prior information. The recursive algorithm involves subtracting a predicted concentration signal arising from a source model consisting of N localized sources from the measured concentration data for increasing values of N and examining the resulting residual data to determine if the residuals are consistent with the estimated noise level in the concentration data. The method is illustrated by application to a real concentration dataset obtained from an atmospheric dispersion experiment involving the simultaneous release of a tracer from four sources.

scholarly journals Ammonia Emissions from Dairy Lagoons in the Western U.S.

2018 ◽  
Vol 61 (3) ◽  
pp. 1001-1015 ◽  
Author(s):  
April B. Leytem ◽  
David L. Bjorneberg ◽  
C. Al Rotz ◽  
Luis E. Moraes ◽  
Ermias Kebreab ◽  
...  
Keyword(s):  
Physicochemical Characteristics ◽  
Ammonia Emission ◽  
Dispersion Modeling ◽  
N Losses ◽  
Total N ◽  
Inverse Dispersion ◽  
South Central ◽  
Liquid Storage ◽  
Wind Events ◽  
On Farm

Abstract. Ammonia (NH3) emissions from dairy liquid storage systems can be a source of reactive nitrogen (N) released to the environment, with a potential to adversely affect sensitive ecosystems and human health. However, little on-farm research has been conducted to estimate these emissions and determine the factors that may affect these emissions. Six lagoons in south-central Idaho were monitored for one year using open-path Fourier transform spectrometry, with NH3 emissions estimated using inverse dispersion modeling (WindTrax software). Lagoon physicochemical characteristics thought to contribute to NH3 emissions were also monitored over this period. Average total emissions from the lagoons ranged from 12 to 43 kg NH3 ha-1 d-1, or 5.4 to 85 kg NH3 d-1. Emissions from the settling basin on one dairy were 30% of the total emissions from the liquid storage system, indicating that basins are important sources of on-farm NH3 emissions. Emissions generally trended greater during the summer, when temperatures were greater. High wind events and agitation of the lagoons created temporary increases in NH3 emissions irrespective of temperature. Lagoon physicochemical characteristics, such as total Kjeldahl nitrogen (TKN) and total ammoniacal nitrogen (TAN), were highly correlated with emissions (r = 0.52 and 0.55, respectively). Regression models were developed to predict on-farm NH3 emissions and indicated that TKN, TAN, wind speed, air temperature, and pH were the main drivers of these emissions. An on-farm N balance suggested that lagoon NH3-N losses represented 9% of total N lost from the facility, 65% of total lagoon N, and 5% of dairy herd N intake. A process-based model (Integrated Farm System Model) estimated values for N excretion and NH3-N loss from the lagoon within 5% of that measured on-farm. More on-farm research is needed to better refine both process-based models and emission factor estimates to more accurately predict NH3 emissions from lagoons on dairies in the western U.S. Keywords: Ammonia, Emission, Inverse dispersion, Manure.

scholarly journals Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling

2020 ◽  
Author(s):  
Julian Kostinek ◽  
Anke Roiger ◽  
Maximilian Eckl ◽  
Alina Fiehn ◽  
Andreas Luther ◽  
...  
Keyword(s):  
Coal Mine ◽  
Regional Scale ◽  
Early Summer ◽  
Data Availability ◽  
Dispersion Modeling ◽  
Emission Rates ◽  
Coal Mine Methane ◽  
Facility Level ◽  
German Aerospace

Abstract. Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH4). The strong localization of CH4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448/720 kt CH4 yr−1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) make the USCB a prime research target for validating and improving CH4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g. single facilities) to regional-scale sources (e.g. agglomerations) in the context of the CoMet 1.0 campaign in early summer 2018. A Quantum Cascade/Interband Cascade Laser (QCL/ICL) based spectrometer adapted for airborne research was deployed aboard the German Aerospace Centers (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights, due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility level estimates. Emission rates from individual sources are not only needed for unambiguous comparisons between bottom-up and top-down inventories but become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional scale and facility level emission estimates. We find instantaneous emission estimates of 452/442 ± 78/75 kt CH4 yr−1 for the morning/afternoon flight of June 6th, 2018. The derived emission rates coincide (±2 %) with annual-average inventorial data from E-PRTR 2017 albeit they are distinctly lower (−37 %/−40 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories.

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