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Analysis and Modelling of Taste and Odour Events in a Shallow Subtropical Reservoir

机译：浅亚热带水库味道和气味事件的分析和建模

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Understanding and predicting Taste and Odour events is as difficult as critical for drinking water treatment plants. Following a number of events in recent years, a comprehensive statistical analysis of data from Lake Tingalpa (Queensland, Australia) was conducted. Historical manual sampling data, as well as data remotely collected by a vertical profiler, were collected; regression analysis and self-organising maps were the used to determine correlations between Taste and Odour compounds and potential input variables. Results showed that the predominant Taste and Odour compound was geosmin. Although one of the main predictors was the occurrence of cyanobacteria blooms, it was noticed that the cyanobacteria species was also critical. Additionally, water temperature, reservoir volume and oxidised nitrogen availability, were key inputs determining the occurrence and magnitude of the geosmin peak events. Based on the results of the statistical analysis, a predictive regression model was developed to provide indications on the potential occurrence, and magnitude, of peaks in geosmin concentration. Additionally, it was found that the blue green algae probe of the lake’s vertical profiler has the potential to be used as one of the inputs for an automated geosmin early warning system.
机译：对于饮用水处理厂而言，了解和预测味觉和气味事件同等重要。近年来发生了许多事件，对廷加尔帕湖（澳大利亚昆士兰州）的数据进行了全面的统计分析。收集了历史手动采样数据以及由垂直轮廓仪远程收集的数据;回归分析和自组织图用于确定味觉和气味化合物与潜在输入变量之间的相关性。结果表明，味道和气味的主要成分是土臭素。尽管主要的预测因素之一是蓝藻水华的发生，但是注意到蓝藻菌种也很关键。此外，水温，储层容量和氧化氮的利用率是决定土臭素峰值事件的发生和程度的关键因素。根据统计分析的结果，开发了预测回归模型，以提供有关土臭素浓度峰值的潜在发生率和幅度的指示。此外，还发现该湖垂直剖面仪的蓝绿色藻类探针有可能被用作自动化土臭味早期预警系统的输入之一。

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