Can Mass Balance Be Trusted in Estimating N Loss for Meat-Poultry Housing?

Quantification of ammonia loss from animal feeding operations by measuring gaseous concentration and air exchange through the emitting source is not always practical, e.g., under natural ventilation conditions. Mass balance over an extended period of time may offer possibilities of remedy. This study compares two NH3-N emission estimate approaches for a commercial turkey grow-out house over one year period: a) a concentration-flow-integration (CFI) method (considered as the reference method), and b) a nitrogen (N) mass-balance method. The CFI NH3-N emission was determined by continuously measuring the NH3 concentration and exhaust air flow rate through the turkey house with a state-of-the-art mobile air emission unit. The mass-balance N emission was calculated by balancing the total N inputs (new bedding, young birds, feed) and N output (litter cake removed between flocks, litter removed at cleanout, amount of marketed birds, mortality, and body N content). The production-related data were acquired from the records kept or presented to the cooperative producer. The results revealed unexpectedly large discrepancy in NH3-N loss between the two methods. The outcome of this study cast serious doubt about the adequacy of using mass balance for estimating NH3 emissions from a dynamic production system such as turkey houses

Evaluation of three pollutant dispersion models for the environmental assessment of a district in Kocaeli, Turkey

Air Quality Modeling is a method used to manage urban air quality. Various pollutant dispersion models are available, and each of these models is characterized by its own advantages and disadvantages. Thus, we aimed to evaluate the advantages and disadvantages of the models and to determine their performance by applying them to a specific district. This study also enabled the determination of the contribution of pollution sources to the total pollution and the current air quality of the study area according to the selected pollutants. In this study, both steady-state models (the American Meteorological Society/Environmental Protection Agency Regulatory Model-AERMOD and the Industrial Source Complex Short Term Model-ISCST-3) and the Lagrangian model (the California Puff Model-CALPUFF) were used as the dispersion models. The Korfez district of Kocaeli was selected as the study area. SO2 and PM10 emissions were observed as pollutants. The statistical methods of mean squared error (MSE) and fractional bias (FB) were employed to evaluate the performance of these models. The results of the study revealed that the highest concentration varied according to the models and time options. However, when the modeling results for all of the sources were examined, the highest concentration was calculated by ISCST-3. The effect of the line source was less than the other sources (point and area). The contributions of the pollution sources differed according to each modeling program. The results of the statistical methods, which were used for evaluating the performance of the models, varied according to both the pollutant type and the time option. An overall ranking regarding modeling performance is as follows: CALPUFF > AERMOD > ISCST-3 for PM10 and ISCST-3 > CALPUFF > AERMOD for SO2. The MSE/FB results demonstrated that the predicted values were lower than the measured outcomes. Similarly, a comparison of the predicted and measured values with national and international limits revealed that various measures are necessary to reduce SO2 and PM10

Determining Performance and application of steady-state models and lagrangian puff model for environmental assessment of CO and NOx emissions

Air quality modellings are highly useful systems used to investigate the possible impact of emissions diffusing into the atmosphere in any area they might have on that area. There are many modelling methods whose capacities are limited by their advantages and disadvantages or the equipment they use. In this study, therefore, both steady-state models (AERMOD and ISCST-3) and the Lagrangian model (CALPUFF) are used. This study has two purposes: one is to specify performance of the models. Performances were determined with various statistical methods such as fractional bias (FB), mean squared error (MSE), and geometric mean bias (MG). The other purpose of this study is to evaluate temporal and spatial variations of point (P), area (A), and line (L) - sourced CO and NOx emissions in the research area by using the modelling methods. The district of Korfez, which is one of the districts of the province of Kocaeli, was chosen as the study area. When the results obtained with modelling all P and A sources by three programs are analyzed, the highest annual concentration AERMOD, ISCST-3, and CALPUFF were found as 128.82, 86.96, and 201.30 mu g/m(3) for CO, and 7.56, 26.31, and 6.10 mu g/m(3) for NON, respectively. On the other hand, when the results obtained with modelling all P and A and L sources by two programs are investigated, the highest annual concentration AERMOD and ISCST-3 were found to be 155.12, 92.46 mu g/m(3) for CO, and 166.93 and 89.98 mu g/m(3) for NOR, respectively. When contributions of the pollutant sources on pollution are evaluated, it was observed that area sources and line sources are more predominant than other sources for CO and NOx emissions. It was observed by analyzing the diffusion maps that residential areas in the district are more concentrated. Therefore, in the study the predicted and observed values were also compared with national and international limit values and determined to meet these limit values. According to the results obtained by evaluation of performances of the models with FB, MS, and MG statistical methods, performance sorting for NOx emissions was found to be ISCST-3 > CALPUFF > AERMOD, while for CO emissions it is given as CALPUFF >AERMOD > ISCST-3. However, since it is not correct to distinguish between performance of a model for an application and that of another model accurately, performances of the models were interpreted according to the results of this study and literature review

Accidental release of Liquefied Natural Gas in a processing facility: Effect of equipment congestion level on dispersion behaviour of the flammable vapour

An accidental leakage of Liquefied Natural Gas (LNG) can occur during processes of production, storage andtransportation. LNG has a complex dispersion characteristic after release into the atmosphere. This complexbehaviour demands a detailed description of the scientific phenomena involved in the dispersion of the releasedLNG. Moreover, a fugitive LNG leakage may remain undetected in complex geometry usually in semi-confined orconfined areas and is prone to fire and explosion events. To identify location of potential fire and/or explosionevents, resulting from accidental leakage and dispersion of LNG, a dispersion modelling of leakage is essential.This study proposes a methodology comprising of release scenarios, credible leak size, simulation, comparison ofcongestion level and mass of flammable vapour for modelling the dispersion of a small leakage of LNG and itsvapour in a typical layout using Computational Fluid Dynamics (CFD) approach. The methodology is applied to acase study considering a small leakage of LNG in three levels of equipment congestion. The potential fire and/orexplosion hazard of small leaks is assessed considering both time dependent concentration analysis and areabased model. Mass of flammable vapour is estimated in each case and effect of equipment congestion on sourceterms and dispersion characteristics are analysed. The result demonstrates that the small leak of LNG can createhazardous scenarios for a fire and/or explosion event. It is also revealed that higher degree of equipmentcongestion increases the retention time of vapour and intensifies the formation of pockets of isolated vapourcloud. This study would help in designing appropriate leak and dispersion detection systems, effective monitoring procedures and risk assessmen

Analysis of Spatial Uncertainty in LiDAR-derived Building Data and Uncertainty Propagation in Modeling of Urban Atmospheric Dispersion

Results of environmental models (EMs) are often used to assist decision making. However, EM outcomes vary significantly with different input data, model parameters and model assumptions. Therefore, informed decision making requires an in-depth understanding of how the changes in input data, model parameters and model assumptions influence the model outputs. While EMs are now accustomed to geo-spatial data, the influences of spatial uncertainty are often overlooked. This research examines the influence of spatial uncertainty throughout the three stages of general environment modeling: 1) examine the uncertainty in geo-spatial data as representation of the environment, 2) examine the uncertainty in the linkage between EMs and Geographic Information System (GIS) and, 3) examine and compare the influence of spatial uncertainty with the uncertainty of model parameters. LiDAR data and urban atmospheric dispersion model (UADM) are used as a use case, to demonstrate the methods and benefits of examining the influence of spatial uncertainty toward EMs

Modeling contaminant transport and fate and subsequent impacts on ecosystems

Assessing risks associated with the release of metals into the environment and managing remedial activities requires simulation tools that depict speciation and risk with accurate mechanistic models and well-defined transport parameters. Such tools need to address the following processes: (1) aqueous speciation, (2) distribution mechanisms, (3) transport, and (4) ecological risk. The primary objective of this research is to develop a simulation tool that accounts for these processes. Speciation in the aqueous phase can be assessed with geochemical equilibrium models, such as MINEQL+. Furthermore, metal distribution can be addressed mechanistically. Studies with Pb sorption to amorphous aluminum (HAG), iron (HFO), and manganese (HMO) oxides, as well as oxide coatings, demonstrated that intraparticle diffusion is the rate-limiting mechanism in the sorption process, where best-fit surface diffusivities ranged from 10-18 to 10-15 cm2 s-1 Intraparticle surface diffusion was incorporated into the Groundwater Modeling System (GMS) to accurately simulate metal contaminant mobility where oxides are present. In the model development, the parabolic concentration layer approximation and the operator split technique were used to solve the microscopic diffusion equation coupled with macroscopic advection and dispersion. The resulting model was employed for simulating Sr90 mobility at the U.S. Department of Energy (DOE) Hanford Site. The Sr90 plume is observed to be migrating out of the 100-N area extending into other areas of the Hanford Site and beyond. Once bioavailability is understood, static or dynamic ecological risk assessments can be conducted. Employing the ERA model, a static ecological risk assessment for exposure to depleted uranium (DU) at Aberdeen and Yuma Proving Grounds (APG and YPG) revealed that a reduction in plant root weight is considered likely to occur. For most terrestrial animals at YPG, the predicted DU dose is less than that which would result in a decrease in offspring. However, for the lesser long-nosed bat, reproductive effects are expected to occur through the reduction in size and weight of offspring. At APG, based on very limited data, it is predicted that uranium uptake will not likely affect survival of terrestrial animals and aquatic species. In model validation, sampling of pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil showed that body burden concentrations fall into the distributions simulated at both sites. This static risk assessment provides a solid background for applying the dynamic approach. Overall, this research contributes to a holistic approach in developing accurate mechanistic models for simulating metal contaminant mobility and bioavailability in subsurface environments

Uncertainty analysis in ecological risk assessment modeling

A probabilistic approach employing Monte Carlo simulations for assessing parameter and risks as probabilistic distributions was used in an ecological risk assessment (ERA) model to characterize risk and address uncertainty. This study addresses the following sources of uncertainty: parameter inputs in the ERA models, risk algorithms and uncertain input concentrations. To achieve this objective, both sensitivity and uncertainty analyses are being conducted. Monte Carlo simulations were used for generating probabilistic distributions of parameter and model uncertainty. All sensitivity, uncertainty, and variability analyses were coded in Visual Basic as part of the ERA model software version 2001, which was developed under the Sustainable Green Manufacturing (SGM) program. This simulation tool includes a Window\u27s based interface, an interactive and modifiable database management system (DBMS) that addresses the food web at trophic levels, and a comprehensive evaluation of exposure pathways. To verify this model, ecological risks from Cr, Ta, Mo and DU exposure at the U.S. Army Yuma Proving Ground (YPG) and Aberdeen Proving Ground (APG) were assessed and characterized. For the case of DU exposure to YPG terrestrial plants, the overall distributions for DU uptake for plants suggest 90% likelihood in reduction in root weight. For most terrestrial animals at YPG, the dose is less than that resulting in a decrease in offspring. At APG, DU exposure potentially poses little risk for terrestrial animals, which is no observable impact on receptor\u27s reproduction or development. DU potentially poses lower risks to aquatic species at APG as well. The overall risk posed by the metals followed the order of Mo\u3eCr\u3eTa for both YPG and APG sits. Blacktailed-jackrabbits, lesser long-nosed bats, mule deer and cactus mice, at YPG site, are expected to have a reduction in size and weight of offspring. Terrestrial plants are likely to exhibit a reduction in root weight. For APG site, the vulnerable receptors are white-footed mice, white-tailed deer, and cottontail rabbits. For terrestrial plants, the risk result suggests a reduction in root weight. Aquatic species did not show any observable risk from Mo, Cr, and Ta in the terms of survival, growth and mortality

Back-calculating emission rates for ammonia and particulate matter from area sources using dispersion modeling

Engineering directly impacts current and future regulatory policy decisions. The foundation of air pollution control and air pollution dispersion modeling lies in the math, chemistry, and physics of the environment. Therefore, regulatory decision making must rely upon sound science and engineering as the core of appropriate policy making (objective analysis in lieu of subjective opinion). This research evaluated particulate matter and ammonia concentration data as well as two modeling methods, a backward Lagrangian stochastic model and a Gaussian plume dispersion model. This analysis assessed the uncertainty surrounding each sampling procedure in order to gain a better understanding of the uncertainty in the final emission rate calculation (a basis for federal regulation), and it assessed the differences between emission rates generated using two different dispersion models. First, this research evaluated the uncertainty encompassing the gravimetric sampling of particulate matter and the passive ammonia sampling technique at an animal feeding operation. Future research will be to further determine the wind velocity profile as well as determining the vertical temperature gradient during the modeling time period. This information will help quantify the uncertainty of the meteorological model inputs into the dispersion model, which will aid in understanding the propagated uncertainty in the dispersion modeling outputs. Next, an evaluation of the emission rates generated by both the Industrial Source Complex (Gaussian) model and the WindTrax (backward-Lagrangian stochastic) model revealed that the calculated emission concentrations from each model using the average emission rate generated by the model are extremely close in value. However, the average emission rates calculated by the models vary by a factor of 10. This is extremely troubling. In conclusion, current and future sources are regulated based on emission rate data from previous time periods. Emission factors are published for regulation of various sources, and these emission factors are derived based upon back-calculated model emission rates and site management practices. Thus, this factor of 10 ratio in the emission rates could prove troubling in terms of regulation if the model that the emission rate is back-calculated from is not used as the model to predict a future downwind pollutant concentration