000227552 001__ 227552
000227552 005__ 20181203024639.0
000227552 0247_ $$2doi$$a10.1039/c6em00584e
000227552 022__ $$a2050-7887
000227552 02470 $$2ISI$$a000398110700026
000227552 037__ $$aARTICLE
000227552 245__ $$aA computer-based prediction platform for the reaction of ozone with organic compounds in aqueous solution: kinetics and mechanisms
000227552 260__ $$bRoyal Soc Chemistry$$c2017$$aCambridge
000227552 269__ $$a2017
000227552 300__ $$a12
000227552 336__ $$aJournal Articles
000227552 520__ $$aOzonation of secondary wastewater effluents can reduce the discharge of micropollutants by transforming their chemical structures. Therefore, a better understanding of the formation of transformation products during ozonation is important. In this study, a computer-based prediction platform for the kinetics and mechanisms of the reactions of ozone with organic compounds was developed to enable in silico predictions of transformation products. With the developed prediction platform, reaction kinetics expressed as second-order rate constants for the reactions of ozone with selected organic compounds (k(O3), M-1 s(-1)) can be predicted based on an adapted k(O3) prediction model from a previous study (Lee et al., Environ. Sci. Technol., 2015, 49, 9925-9935) (average model error of about a factor of 6 for 14 compound classes with 284 model compounds). Ozone reaction mechanisms reported in the literature have been reviewed and, using chemoinformatics tools, encoded into about 340 individual reaction rules that can be generally applied to predict the transformation products of micropollutants. Predictions for k(O3) and/or transformation products were overall consistent with the experimental data for three micropollutants used as validation compounds (e.g., carbamazepine, tramadol, and triclosan). However, limitations of the current k(O3) prediction platform were also identified: ambiguous assignment of the n-th highest occupied molecular orbital energy (EHOMO-n) to the reactive sites, potential errors associated with the use of a gas-phase geometry, and a poor k(O3) prediction for certain compounds (cetirizine). Therefore, the current prediction tool should not be considered as a substitute for experimental studies and experimental data are still required in the future to obtain a more robust prediction model. Nonetheless, the developed prediction platform, made available as a stand-alone graphical user interface (GUI) application, will provide useful information about aqueous ozone chemistry to various groups of end-users such as environmental chemists, engineers, or toxicologists.
000227552 700__ $$0245981$$g218927$$uEcole Polytech Fed Lausanne, Sch Architecture Civil & Environm Engn ENAC, CH-1015 Lausanne, Switzerland$$aLee, Minju
000227552 700__ $$uSwiss Fed Inst Technol, Sci IT Serv SIS, Zurich, Basel, Switzerland$$aBlum, Lorenz C.
000227552 700__ $$uSwiss Fed Inst Technol, Sci IT Serv SIS, Zurich, Basel, Switzerland$$aSchmid, Emanuel
000227552 700__ $$uEawag Swiss Fed Inst Aquat Sci & Technol, Ueberlandstr 133, CH-8600 Dubendorf, Switzerland$$aFenner, Kathrin
000227552 700__ $$aVon Gunten, Urs$$g210253$$0245482
000227552 773__ $$j19$$tEnvironmental Science-Processes & Impacts$$k3$$q465-476
000227552 909C0 $$xU12400$$0252250$$pLTQE
000227552 909CO $$particle$$pENAC$$ooai:infoscience.tind.io:227552
000227552 917Z8 $$x106743
000227552 937__ $$aEPFL-ARTICLE-227552
000227552 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000227552 980__ $$aARTICLE