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ndltd-OhioLink-oai-etd.ohiolink.edu-toledo13155821282021-08-03T06:08:03Z Dispersion Modeling and Characterization of Particulates from Land Application of Class B Biosolids Bhat, Abhishek S. This study presents a comprehensive approach to understand the particle characteristics, identify the source profile, develop new equations for emission rates, analyze the source-receptor relationship, and develop and evaluate a numerical model for the dispersion and transport of particles released during the injection of biosolids. The approach uniquely helps in understanding the particle characteristics by revealing the concentration trends and modality, by establishing the size and shape distribution, by determining the elemental contribution, and by identifying the source profiles. The extensive literature study indicates that the above topics have not been examined in detail for the particulate emission released during the injection of biosolids on a farm field. Two field studies were conducted in the summer of 2008 and 2009 to collect airborne particulate matter emitted during the injection application of class B biosolids. The sampling was carried out before (pre-application), during (application), and after (post-application) the application. The research work characterized the particulate emissions deposited on the aerosols spectrometer. The mass concentrations of fine (PM2.5) and ultrafine (PM1.0) particles were highest during the pre-application. The mass concentration of thoracic fraction (PM2.5-10) increased significantly during the application. A bimodal size distribution was observed throughout the sampling. Nuclei mode formation was predominant during the pre-application and the post-application, whereas the accumulation mode was distinctive during the application. Emissions of course particles increased during the application. It was also observed that the ultrafine and fine particles travelled longer downwind distances during the application. Emission rates for the particle size range 0.23 µm to 20.0 µm were determined for different agricultural activities pre-application, application, and post-application. The whisker plots of the daily emission rates concluded that the emissions were highest during the application and lowest during the post-application. A comparison of calculated emission rates to those reported in literature was done. Overall, the agricultural activities involving injection application produced higher emission rates as compared to the emission rates reported in the literature. Airborne particles were collected on filter papers during the biosolids application process using an aerosol spectrometer. Scanning electron microscopy (SEM) coupled with an energy dispersive spectrometry (EDS) tool was used to analyze particles collected before, during, and after injection of biosolids. The major emphasis of the analysis was on providing in depth information on particle count, size, shape, morphology, and chemical composition. The particle count was significantly sensitive towards the different activities surrounding the application. The number of particles emitted during the application was almost three times that emitted during the pre-application. A significant correlation was observed between different activities and size distribution of particles. A majority of the particles emitted during the application had a diameter greater than 5 µm. Concentrations of particles with higher diameter (> 10 µm) were highest during the application. A significant increase in polygon and agglomerate shape particles was observed during the application as compared to the pre-application and the post-application. The elemental composition of particles obtained by EDS spectra suggests that the particles collected contained elements such as C, O, Na, Mg, Al, Si, S, K, Ca, Fe, Cu, and Zn. It was observed that Cr, Pb, P, Cd and Mn were present only in particles collected during the application and, were not detected during the pre-application or the post-application. Presence of these elements during the application could be attributed to the injection of biosolids. The combination of SEM, particle analysis software, and EDS technique was capable of revealing detailed information on the size, shape, morphology, and chemical composition of individual particles. These techniques proved to be an effective non-destructive method for the analysis. To further understand the contribution of the biosolids application as a source, a detailed source apportionment study was conducted. The filter papers were analyzed using ICP-OES for elemental concentrations. The average elemental concentration of US EPA regulated elements such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), nickel (Ni), selenium (Se), and titanium (Ti) increased significantly during the application. Increase in the macronutrients, such as phosphorus (P), sulfur (S), calcium (Ca), and magnesium (Mg); and some of the micronutrients, such as copper (Cu), iron (Fe), and zinc (Zn), make biosolids a viable, cheap, and sustainable partial or full replacement to commercial fertilizers. The receptor modeling using the positive matrix factorization was used to identify and characterize the source profiles contributing to the emissions. Four major sources contributing to the emissions were identified as industrial source, soil/dust, biosolids, and ambient/background. The study revealed that biosolids source contributed significantly to the emissions of aluminum (85%), cadmium (59%), chromium (96%), nickel (45%), lead (49%), and silicon (62%). The source profile will be helpful to the regulators and policy makers to promote the use of biosolids for land application. The short range dispersion of particulate matter from a ground level area source is difficult to simulate. The dissertation examines the particle dispersion in the nearby area from injection of the biosolids an open field. A computational fluid dynamics (CFD) model has been developed to simulate the particle dispersion. A commercial CFD package FLUENT was used to model the physical phenomenon. The results of the simulations were compared to the observed concentrations at two different downwind distances collected during the 2009 field study. The performance of the model is evaluated using four statistical parameters (normalized mean square error (NMSE), fractional bias (FB), correlation coefficient (R), and geometric mean (GM). The results are also compared with an analytical model (BDRM 1.1). The comparison and statistical evaluation of the model predictions suggests that the numerical model was successful in simulating under unstable atmospheric conditions. The numerical model predictions were closer to the observed concentrations as compared to the predictions from the analytical model. 2011 English text University of Toledo / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=toledo1315582128 http://rave.ohiolink.edu/etdc/view?acc_num=toledo1315582128 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
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