Ye Bixiong, Wang Wuyi, Yang Linsheng, Wei Jianrong, E Xueli
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
J Environ Monit. 2011 May;13(5):1271-5. doi: 10.1039/c0em00795a. Epub 2011 Mar 18.
Water quality parameters including TOC, UV(254), pH, chlorine dosage, bromide concentration and disinfection by-products were measured in water samples from 41 water treatment plants of six selected cities in China. Chloroform, bromodichloromethane, dibromochloromethane, dichloroacetic acid and trichloroacetic acid were the major disinfection by-products in the drinking water of China. Bromoform and dibromoacetic acid were also detected in many water samples. Higher concentrations of trihalomethanes and haloacetic acids were measured in summer compared to winter. The geographical variations in DBPs showed that TTHM levels were higher in Zhengzhou and Tianjin than other selected cities. And the HAA5 levels were highest in Changsha and Tianjin. The modeling procedure that predicts disinfection by-products formation was studied and developed using artificial neural networks. The performance of the artificial neural networks model was excellent (r > 0.84).
在中国六个选定城市的41家水处理厂采集的水样中,对包括总有机碳(TOC)、紫外线吸光度(UV(254))、pH值、氯剂量、溴化物浓度和消毒副产物在内的水质参数进行了测量。氯仿、溴二氯甲烷、二溴氯甲烷、二氯乙酸和三氯乙酸是中国饮用水中的主要消毒副产物。在许多水样中还检测到了溴仿和二溴乙酸。与冬季相比,夏季测量到的三卤甲烷和卤乙酸浓度更高。消毒副产物的地理差异表明,郑州和天津的总三卤甲烷水平高于其他选定城市。而五卤乙酸水平在长沙和天津最高。利用人工神经网络研究并开发了预测消毒副产物形成的建模程序。人工神经网络模型的性能优异(r > 0.84)。