Secondary organic aerosol formation from m-xylene, toluene, and benzene

Secondary organic aerosol (SOA) formation from the photooxidation of m-xylene, toluene, and benzene is investigated in the Caltech environmental chambers. Experiments are performed under two limiting NOx conditions; under high-NOx conditions the peroxy radicals (RO2) react only with NO, while under low-NOx conditions they react only with HO2. For all three aromatics studied (m-xylene, toluene, and benzene), the SOA yields (defined as the ratio of the mass of organic aerosol formed to the mass of parent hydrocarbon reacted) under low-NOx conditions substantially exceed those under high-NOx conditions, suggesting the importance of peroxy radical chemistry in SOA formation. Under low-NOx conditions, the SOA yields for m-xylene, toluene, and benzene are constant (36%, 30%, and 37%, respectively), indicating that the SOA formed is effectively nonvolatile under the range of Mo(>10 μg m−3) studied. Under high-NOx conditions, aerosol growth occurs essentially immediately, even when NO concentration is high. The SOA yield curves exhibit behavior similar to that observed by Odum et al. (1996, 1997a, b), although the values are somewhat higher than in the earlier study. The yields measured under high-NOx conditions are higher than previous measurements, suggesting a "rate effect" in SOA formation, in which SOA yields are higher when the oxidation rate is faster. Experiments carried out in the presence of acidic seed aerosol reveal no change of SOA yields from the aromatics as compared with those using neutral seed aerosol

Application of Probabilistic Neural Networks in Modelling Structural Deterioration of Stormwater Pipes

In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number of city councils regarding their deterioration. A study has been conducted on the structural deterioration of concrete pipes that make up the bulk of the stormwater pipe systems in these councils. In an attempt to look for a reliable deterioration model, a probabilistic neural network (PNN) model was developed using the data set supplied from participating councils. The PNN model was validated with snapshot-based sample data, which makes up the data set. The predictive performance of the PNN model was compared with a traditional parametric model using discriminant analysis on the same data set. Structural deterioration was hypothesised to be influenced by a set of explanatory factors, including pipe design and construction factors—such as pipe size, buried depth—and site factors— such as soil type, moisture index, tree root intrusion, etc. The results show that the PNN model has a better predictive power and uses significantly more input variables (i.e., explanatory factors) than the discriminant model. More importantly, the key factors for prediction in the PNN model are difficult to interpret, suggesting that besides prediction accuracy, model interpretation is an important issue for further investigation

Development of mainshaft seals for advanced air breathing propulsion systems, phase 1 Final report, 25 Jun. 1965 - 25 Jul. 1967

Comparison of gas film mainshaft seals with rubbing contract seals for high temperature, high speed, and high pressure gas turbine application