Source term modeling is the key feature in predicting the consequences of releases from hazardous fluids. Aqueous ammonia serves the purpose of a reducing medium and is replacing anhydrous ammonia in most of the Selective catalytic reduction (SCR) units. This newly developed model can estimate the vaporization rate and net mass evaporating into the air from a multicomponent non- ideal chemical spill. The work has been divided into two parts. In the first step a generic, dynamic source term model was developed that can handle multicomponent non-ideal mixtures. The applicability of this improved pool model for aqueous ammonia spills was then checked to aid in the offsite consequence analysis of aqueous ammonia spills. The behavior of the chemical released depends on its various inherent properties, ambient conditions and the spill scenario. The different heat transfer mechanisms associated with the pool will strongly depend on the temperature of the liquid pool system at different times. The model accounts for all the temperature gradients within the contained pool and hence helps us establish better estimation techniques for source terms of chemical mixtures. This research work will help obtain more accurate and reliable liquid evaporation rates that become the critical input for dispersion modeling studies.

Today's energy production is changing from scheduled to intermittent generation due to the increasing energy injection from renewable sources. This alteration requires flexibility in energy generation and demand. Electric heat pumps and thermal storages were found to have a large potential to provide demand flexibility which is analysed in this work. A three-fold method is set up to generate thermal load profiles, to simulate heat pump pools and to assess heat pump flexibility. The thermal profile generation based on a combination of physical and behavioural models is successfully validated against measurement data. A randomised system sizing procedure was implemented for the simulation of heat pump pools. The parameter randomisation yields correct seasonal performance factors, full load hours and average operation cycles per day compared to 87 monitored systems. The flexibility assessment analysis the electric load deviation of representative heat pump pool in response to 5 different on / off signals. The flexibility is induced by the capacity of thermal storages and analysed by four parameters. Generally, on signals are more powerful than off signals. A generic assessment by the ambient temperature yield that the flexibility is highest for heating days and the activated additional space heating storage: Superheating of the storage to the maximal temperature provides a flexible energy of more than 400 kWh per 100 heat pumps in a temperature range between -10 and +13 °C.

Soil organic matter (SOM) is the largest terrestrial pool of organic carbon (C), and potential carbon-climate feedbacks involving SOM decomposition could exacerbate anthropogenic climate change. Despite the importance of SOM in the global C cycle, our understanding of the controls on SOM stabilization and decomposition is still developing, and as such, SOM dynamics are a source of major uncertainty in current Earth system models (ESMs), which reduces the effectiveness of these models in predicting the efficacy of climate change mitigation strategies. To improve our understanding of controls on SOM decomposition at scales relevant to such modeling efforts, A and upper B horizon soil samples from 22 National Ecological Observatory Network (NEON) sites spanning the conterminous U.S. were incubated for 52 weeks under conditions representing site-specific mean summer temperature and horizon-specific field capacity (-33 kPa) water potential. Cumulative CO2 respired was periodically measured and normalized by soil organic C content to obtain cumulative specific respiration (CSR). A two-pool decomposition model was fitted to the CSR data to calculate decomposition rates of fast- (kfast) and slow-cycling pools (kslow). Post-LASSO best subsets multiple linear regression was used to construct horizon-specific models of significant predictors for CSR, kfast, and kslow. Significant predictors for all three response variables consisted mostly of proximal factors related to clay-sized fraction mineralogy and SOM composition. Non-crystalline minerals and lower SOM lability negatively affected CSR for both A and B horizons. Significant predictors for decomposition rates varied by horizon and pool. B horizon decomposition rates were positively influenced by nitrogen (N) availability, while an index of pyrogenic C had a negative effect on kfast in both horizons. These results reinforce the recognized need to explicitly represent SOM stabilization via interactions with non-crystalline minerals in ESMs, and they also suggest that increased N inputs could enhance SOM decomposition in the subsoil, highlighting another mechanism beyond shifts in temperature and precipitation regimes that could alter SOM decomposition rates. / Master of Science / Soils contain a large amount of carbon (C) in the form of soil organic matter (SOM), and there is the potential for the increased decomposition of SOM due to warmer temperatures to cause climate change to become worse through the release of additional CO₂ into the atmosphere. However, we still do not know exactly what is most important for predicting how vulnerable SOM is to decomposition at continental scales, and this results in a substantial amount of uncertainty in Earth system models used to predict climate change. To address this question, the proportion of organic C decomposed in soil samples from the topsoil and subsoil from 22 sites across the conterminous U.S. was monitored over the course of a year under optimal moisture conditions and at site-specific summer temperature. Additionally, a mathematical model was fitted to the proportion of organic C decomposed over time to estimate decomposition rates of a quickly decomposing pool of SOM and a slowly decomposing pool of SOM. The proportion of organic C decomposed and decomposition rates were related to soil and site properties using multiple linear regression to find which soil and site properties were most important for predicting these response variables. The type of clay-sized mineral and SOM chemical composition were found to be important predictors of the proportion of organic C decomposed for both topsoil and subsoil samples. The important predictors for decomposition rates varied by pool and by topsoil vs. subsoil. For subsoil decomposition rates, it was found that a greater availability of nitrogen (N) increased decomposition rates, and in the quickly decomposing pool, it was found that fire-derived organic matter slowed decomposition rates. The results of this study showed the general importance of local factors for controlling SOM decomposition. Specifically, it showed that the type of clay-sized mineral present at a site needs to be considered as well as the fact that N might increase SOM decomposition in the subsoil.