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Time-frequency Analysis Of Beach Bacteria Variations And Its Implication For Recreational Water Quality Modeling

机译：沙滩细菌变异的时频分析及其在休闲水质建模中的意义

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This paper exploited the potential of the wavelet analysis in resolving beach bacteria concentration and candidate explanatory variables across multiple time scales with temporal information preserved. The wavelet transform of E. coli concentration and its explanatory variables observed at Huntington Beach, Ohio in 2006 exhibited well-defined patterns of different time scales, phases, and durations, which cannot be clearly shown in conventional time-domain analyses. If linear regression modeling is to be used for the ease of implementation and interpretation, the wavelet-transformed regression model reveals that low model residual can be realized through matching major patterns and their phase angles between E. coli concentration and its explanatory variables. The property of pattern matching for linear regression models can be adopted as a criterion for choosing useful predictors, while phase matching further explains why intuitively good variables such as wave height and onshore wind speed were excluded from the optimal models by model selection processes in Frick et al. (Environ. Sci. Technol. 2008,42,4818-4824). The phase angles defined by the wavelet analysis in the time-frequency domain can help identify the physical processes and interactions occurring between bacteria concentration and its explanatory variables. It was deduced, for this particular case, that wind events resulted in elevated ￡ coli concentration, wave height, and turbidity at the beach with a periodicity of 7-8 days. Wind events also brought about increased beach bacteria concentrations through large-scale current circulations in the lake with a period of 21 days. The time length for linear regression models with statistical robustness can also be deduced from the periods of the major patterns in bacteria concentration and explanatory variables, which explains and supplements the modeling efforts performed in (1).
机译：本文利用小波分析在解决保留时间信息的多个时间尺度上解决沙滩细菌浓度和候选解释变量方面的潜力。 2006年在俄亥俄州亨廷顿比奇观察到的大肠杆菌浓度的小波变换及其解释变量表现出不同时间尺度，阶段和持续时间的清晰定义的模式，这在常规时域分析中无法清晰显示。如果要使用线性回归模型简化实施和解释，则小波变换的回归模型表明，通过匹配主要模式及其在大肠杆菌浓度与其解释变量之间的相角，可以实现低模型残差。线性回归模型的模式匹配特性可以用作选择有用预测变量的标准，而相位匹配则进一步说明了为什么Frick等人的模型选择过程会从最佳模型中直观地排除好波浪高度和陆上风速等变量等（环境科学技术.2008,42,4818-4824）。小波分析在时频域中定义的相角可以帮助识别细菌浓度及其解释变量之间发生的物理过程和相互作用。对于这种特殊情况，可以推断出风事件导致海滩上的大肠杆菌浓度，波高和浊度升高，周期为7-8天。风力事件还通过21天的湖中大规模电流环流使海滩细菌浓度增加。具有统计稳健性的线性回归模型的时间长度也可以从细菌浓度和解释变量的主要模式的周期中得出，这解释并补充了（1）中进行的建模工作。

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  • 来源
    《Environmental Science & Technology》 |2009年第4期|1128-1133|共6页
  • 作者

    ZHONGFU GE; WALTER E. FRICK;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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