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Development of Models for Predicting the Predominant Taste and Odor Compounds in Taihu Lake, China

机译:太湖地区主要味觉和气味化合物预测模型的建立

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摘要

Taste and odor (T&O) problems, which have adversely affected the quality of water supplied to millions of residents, have repeatedly occurred in Taihu Lake (e.g., a serious odor accident occurred in 2007). Because these accidents are difficult for water resource managers to forecast in a timely manner, there is an urgent need to develop optimum models to predict these T&O problems. For this purpose, various biotic and abiotic environmental parameters were monitored monthly for one year at 30 sites across Taihu Lake. This is the first investigation of this huge lake to sample T&O compounds at the whole-lake level. Certain phytoplankton taxa were important variables in the models; for instance, the concentrations of the particle-bound 2-methylisoborneol (p-MIB) were correlated with the presence of Oscillatoria, whereas those of the p-β-cyclocitral and p-β-ionone were correlated with Microcystis levels. Abiotic factors such as nitrogen (TN, TDN, NO3-N, and NO2-N), pH, DO, COND, COD and Chl-a also contributed significantly to the T&O predictive models. The dissolved (d) T&O compounds were related to both the algal biomass and to certain abiotic environmental factors, whereas the particle-bound (p) T&O compounds were more strongly related to the algal presence. We also tested the validity of these models using an independent data set that was previously collected from Taihu Lake in 2008. In comparing the concentrations of the T&O compounds observed in 2008 with those concentrations predicted from our models, we found that most of the predicted data points fell within the 90% confidence intervals of the observed values. This result supported the validity of these models in the studied system. These models, basing on easily collected environmental data, will be of practical value to the water resource managers of Taihu Lake for evaluating the probability of T&O accidents.
机译:太湖屡屡发生味道和气味(T&O)问题,严重影响了为数百万居民提供的水的质量(例如,2007年发生了一次严重的气味事故)。由于这些事故使水资源管理人员难以及时进行预测,因此迫切需要开发最佳模型来预测这些T&O问题。为此,在太湖的30个地点每月监测各种生物和非生物环境参数,为期一年。这是对这个在整个湖面上采样T&O化合物的巨大湖泊的首次调查。某些浮游植物类群是模型中的重要变量。例如,颗粒结合的2-甲基异冰片醇(p-MIB)的浓度与颤藻的存在有关,而对β-环柠檬醛和对β-紫罗兰酮的浓度与微囊藻的含量有关。非生物因素(例如氮(TN,TDN,NO3-N和NO2-N),pH,DO,COND,COD和Chl-a)也对T&O预测模型做出了重要贡献。溶解的(d)T&O化合物与藻类生物量和某些非生物环境因素有关,而与颗粒结合的(p)T&O化合物与藻类的存在更紧密相关。我们还使用了先前于2008年从太湖收集的独立数据集测试了这些模型的有效性。在比较2008年观察到的T&O化合物的浓度与根据我们的模型预测的浓度时,我们发现大多数预测数据点落在观测值的90%置信区间内。这一结果支持了这些模型在所研究系统中的有效性。这些模型基于容易收集的环境数据,对于太湖水资源管理者评估输电事故的可能性具有实用价值。

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