Seven transformation products of carbamazepine generated by at least one of three common water treatment technologies (UV-radiation, oxidation with chlorine dioxide (ClO2), and biological treatment with activated sludge) were identified by complementary use of ion trap, single quadrupole, and quadrupole-time-of-flight mass spectrometers. Acridine was formed during all of the three treatment processes, while acridine 9-carbaldehyde was identified as an intermediate during ClO2 oxidation. Further treatment of acridine with ClO2 produced 9-hydroxy-acridine. UV-treatment resulted in the formation of acridone, hydroxy-(9H,10H)-acridine-9-carbaldehyde, acridone-N-carbaldehyde, and 1-(2-benzaldehyde)-(1H,3H)-quinazoline-2,4-dione, while biological breakdown of acridine yielded acridone. In parallel, the transformation product iminostilbene was observed during sample analysis. In addition, this study compared the treatment technologies according to the removal of carbamazepine and the production and decay of its transformation products. The most successful method for the removal of carbamazepine was UV treatment while acridine and acridone were more susceptible to biological treatment. Therefore, based on the enhanced biodegradability of carbamazepine residues achieved by UV irradiation, we propose a coupled treatment technology involving an initial UV treatment step followed by biological treatment which may satisfactorily remove the parent compound and its transformation products.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23–25, 2009 at Stanford University. The titles of the nine symposia were Agents that Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real-World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents that Learn from Human Teachers was to investigate how we can enable software and robotics agents to learn from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more AI-based approaches in event processing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies II AAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic Web symposium focused on the real-world grand challenges in this area. Finally, the Technosocial Predictive Analytics symposium explored new methods for anticipatory analytical thinking that provide decision advantage through the integration of human and physical models.