Towards robust on-site ammonia emission measuring techniques based on 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.

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

10.1117/12.339020 ◽

1999 ◽

Cited By ~ 1

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

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

Transactions of the ASABE ◽

10.13031/trans.12646 ◽

2018 ◽

Vol 61 (3) ◽

pp. 1001-1015 ◽

Cited By ~ 2

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.

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Estimating ammonia emissions from a winter wheat cropland in North China Plain with field experiments and inverse dispersion modeling

Atmospheric Environment ◽

10.1016/j.atmosenv.2015.01.003 ◽

2015 ◽

Vol 104 ◽

pp. 1-10 ◽

Cited By ~ 19

Author(s):

Qing Huo ◽

Xuhui Cai ◽

Ling Kang ◽

Hongsheng Zhang ◽

Yu Song ◽

...

Keyword(s):

Winter Wheat ◽

North China Plain ◽

North China ◽

Field Experiments ◽

Dispersion Modeling ◽

Ammonia Emissions ◽

Inverse Dispersion ◽

Inverse Dispersion Modeling

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Inverse Dispersion Modeling of Bioaerosol Emissions from Manure Application Sites

10.13031/aim.20131594700 ◽

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

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VOC emission source strengths of tankers during refuelling activities determined by spectroscopic remote sensing and inverse dispersion modeling

10.1117/12.454432 ◽

2002 ◽

Author(s):

Klaus Schaefer ◽

Herbert Hoffmann ◽

Igor Dormuth ◽

Carsten Jahn ◽

Stefan M. Emeis

Keyword(s):

Remote Sensing ◽

Emission Source ◽

Dispersion Modeling ◽

Voc Emission ◽

Inverse Dispersion ◽

Inverse Dispersion Modeling

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

ISRN Applied Mathematics ◽

10.5402/2012/465320 ◽

2012 ◽

Vol 2012 ◽

pp. 1-20 ◽

Cited By ~ 17

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.

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Evaluation of ammonia emissions from filtration of digestate used for fertigation

Journal of Agricultural Engineering ◽

10.4081/jae.2021.1187 ◽

2021 ◽

Vol 52 (3) ◽

Author(s):

Celeste Righi Ricco ◽

Alberto Finzi ◽

Viviana Guido ◽

Elisabetta Riva ◽

Omar Ferrari ◽

...

Keyword(s):

Dispersion Model ◽

Farming Systems ◽

Farming System ◽

Passive Samplers ◽

Ammonia Emission ◽

Specific Reference ◽

Ammonia Emissions ◽

Mean Values ◽

Po Valley ◽

Inverse Dispersion

Fertigation can be a suitable technique for utilizing digestate, minimizing nitrogen losses, and contributing to circularity within a farming system. For this purpose, digestate usually is first processed with a screw-press separator. However, further filtration is required to remove particles that could clog the nozzles of drip or sprinkling irrigation systems. Advanced filtration can be obtained using mechanical separation with screens having openings of 100- 300 μm. This operation can be another source of ammonia emission, but this aspect has not been adequately investigated. This study aimed to address this knowledge gap by evaluating the emissions from three different filtration systems for digestate. The study was conducted in three different farms located in Lombardy (Italy) using digestate to fertigate maize by drip irrigation (two farms) and pivot irrigation (one farm). Ammonia emissions were measured with passive samplers and the fluxes were examined using an inverse dispersion model implemented in Windtrax software. The emissions were measured both when the filtration systems were in operation and when they were switched off. Ammonia emissions (mean values between 375 and 876 μg NH3/m2/s) tended to increase during operation of the filtration systems. However, no significant differences were found in the emissions from active and inactive equipment on any of the farms. The emissions from the filtration systems were higher than from a storage tank (22-67 μg NH3/m2/s). However, the mean emissions amounted to only 0.3% of the nitrogen content of the digestate. These emissions can be considered irrelevant in the context of the whole management scheme for digestate. This work provides a first insight on ammonia emissions arising from advanced filtration of digestate, with specific reference to Po Valley farming systems. Further studies are required to improve knowledge about emissions from the entire digestate management process, including the treatments required for specific application techniques.

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