NOx emissions from a Central California dairy

Hasson, Alam S.; Ogunjemiyo, Segun; Trabue, Steven; Ashkan, Shawn; Scoggin, Kenwood; Steele, Julie; Olea, Catalina; Middala, Srikar; Vu, Kennedy; Scruggs, Austen; Addala, Laxmi R.; Nana, Lucien

doi:10.1016/j.atmosenv.2013.01.011

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The results showed that simulated and observed concentrations were in better agreement within 1-5 km than those at receptors situated in 5km further away from the reference point. Moreover, the results revealed that both observed predicted ambient NO 2 concentrations were not exceed the limit values set by the National Ambient Air Quality Standards (Seangkiatiyuth et al, 2011 (Hasson et al, 2013).…”

Section: Introductionmentioning

confidence: 84%

Prediction of No2 Concentrations in a Gas Refinery Using Air Dispersion Modeling

Minabi¹

2017

Appl. Ecol. Env. Res.

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Nitrogen dioxide (NO 2 ) is considered as one of the most important ambient air pollutants in gas refineries. In this study, AERMOD dispersion model was applied for prediction of NO 2 ambient concentrations and dispersion patterns from point sources, including 13 stacks and six flares in a gas refinery located in Asaluyeh, Iran. For this purpose, the NO 2 concentrations exhausted from the stacks were measured by a portable emission analyzer and the NO 2 concentrations resulted from the flares were estimated using the emission factors. Moreover, the amounts of ambient NO 2 concentrations in 10 monitoring stations were measured in four seasons. Then, the ambient NO 2 concentrations and dispersion patterns were predicted using the AERMOD model within a domain of 10 × 10 km 2 , in 1-hr and 12 months averages and the unhealthy zones in the study area were defined. The results revealed that for both annual observed and predicted values, ambient NO 2 concentrations were higher than WHO standard limits but they did not exceed the US EPA standard limits. However, the hourly observed and predicted concentrations were lower than the standard levels. Statistical methods were used for comparing the predicted and observed NO 2 concentrations. Simulation results indicated that the predicted concentrations were underestimated by a factor of 20 % in comparison to the measured ones which revealed the estimated contribution of other sources including mobile sources and neighbor sources located in the vicinity of the gas refinery.

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Section: Introductionmentioning

confidence: 84%

Prediction of No2 Concentrations in a Gas Refinery Using Air Dispersion Modeling

Minabi¹

2017

Appl. Ecol. Env. Res.

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“…2 AERMOD has been used to model the dispersion of contaminant emissions in several studies and research papers. Examples include the modelling of the dispersion of polychlorinated dibenzodioxin emissions from a municipal solid waste bio-drying plant, 3 particulate emissions from multiple coal mines, 4 nitrogen oxide emissions from a commercial dairy facility, 5 nitrogen dioxide and sulphur dioxide emissions from a power plant, 6 and nitrogen dioxide emissions from a cement plant. 7 CALPUFF is a non-steady state Lagrangian puff model that can be used to assess contaminant dispersion on a long-range transport basis.…”

Section: Introductionmentioning

confidence: 99%

A study of the dispersion of vinyl chloride from a renewable energy facility located in Ontario, Canada

Obaid

Abdul‐Wahab

Elkamel

2014

Journal of Renewable and Sustainable Energy

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This study compared the results of modelling vinyl chloride emissions from a renewable energy generation plant, located in the City of Kawartha Lakes, Ontario, Canada, using American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and California Puff (CALPUFF). The plant utilizes landfill gas emissions from the existing Lindsay/Ops landfill to fuel an on-site generator, and thus generate electricity. The modelling was performed for July 20, 2013 and November 17, 2013, from 00h00 local standard time (LST) to 23h00 LST for both days. The results showed that modelling contaminant dispersion using CALPUFF resulted in significantly higher concentrations than when modelling with AERMOD. This may be due to the several limitations of AERMOD, including its assumptions of straight line trajectory, steady-state and a uniform atmosphere, its inaccuracy when determining long-range transport, and its inability to retain the memory of the previous hours' emissions. The CALPUFF modelling results also showed that the contaminant concentration may, in some instances, be higher farther away from the source than closer to it. The results also show that the concentration of vinyl chloride on July 20, 2013, as modelled by CALPUFF, exceeds the applicable regulatory limit. Thus, it is recommended to use CALPUFF instead of AERMOD to assess the dispersion of emissions from a facility more conservatively. Using CALPUFF will also ensure that the dispersion is assessed further away from the emissions sources as well as nearer to them. The appropriate selection of dispersion models is especially important when the results of one of the models may deem the facility's emissions non-compliant with the applicable regulatory limits.

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“…New particle growth occurs from secondary particle formation inside poultry houses, but this formation is not often observed on the short time scales they occur. Gases have been measured and characterized in agricultural settings [15,22,29,30]. Ammonia gas, which is present in high concentrations in CAFOs, forms secondary particles when reacting with sulfate and nitrate in the air, but it is unclear which other gaseous compounds could also form particles.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Particle Size Distributions and Water-Soluble Ions in Particulate Matter Measured at a Broiler Farm

Silva,

Cress,

Drover

et al. 2023

Agriculture

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The chemical composition and size distribution of particulate matter produced at broiler poultry houses is not well understood, so this is a novel study to understand the particulate size distributions at a poultry house as well as the ionic composition of the particulate matter using real-time methods. Two optical particle counters provided particle size distributions inside and outside the house. An ambient ion monitor and a particle-in-liquid sampler analyzed the ionic chemical composition of the particulate matter in the house while a scanning mobility particle sizer provided size information in the nanoparticle range. Ammonia concentrations in the house were measured using a chemical sensor. Ammonia concentrations in the house were consistently in the lower part of the per million range 2–20 ppm. The optical particle counter and ion chromatography measurements both showed a strong diurnal variation of particulate matter concentration in the house throughout the study, associated with the lights being on and animal activity. Particulate mass concentration inside the house was dominated by coarse mode particles as opposed to the outdoor sampler which showed much smaller sizes. A few new particle formation and growth events were observed in the house. Ionic constituents detected by chromatography made up a small fraction of the overall mass concentration. The composition of the ionic constituents was similar for most of the study with typical ions being ammonium, sodium, potassium, chloride, sulfate, nitrate, nitrite, phosphate, and several carboxylates (formate, acetate, propionate, and butyrate.) At the end of the study, bromide was also detected during the last several days. Overall, we determined that the ionic components of the particulate matter formed through secondary particle formation was small, but also that some ionic constituents can be associated with management practices.

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NOx emissions from a Central California dairy

Cited by 6 publications

References 25 publications

Prediction of No2 Concentrations in a Gas Refinery Using Air Dispersion Modeling

Prediction of No2 Concentrations in a Gas Refinery Using Air Dispersion Modeling

A study of the dispersion of vinyl chloride from a renewable energy facility located in Ontario, Canada

Characterization of Particle Size Distributions and Water-Soluble Ions in Particulate Matter Measured at a Broiler Farm

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