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Optimising statistical models to predict faecal pollution in coastal areas based on geographic and meteorological parameters

机译:基于地理和气象参数优化统计模型以预测沿海地区的粪便污染

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摘要

This article describes a methodology for optimising predictive models for concentrations of faecal indicator organisms (FIOs) in coastal areas based on geographic and meteorological characteristics of upstream catchments. Concentrations of FIOs in mussels and water sampled from 50 sites in the south of Brazil from 2012 to 2013 were used to develop models to separately predict the spatial and temporal variations of FIOs. The geographical parameters used in predictive models for the spatial variation of FIOs were human population, urban area, percentage of impervious cover and total catchment area. The R-2 of models representing catchments located within 3.1 km from the monitoring points was up to 150% higher than that for the nearest catchment. The temporal variation of FIOs was modelled considering the combined effect of meteorological parameters and different time windows. The explained variance in models based on rainfall and solar radiation increased up to 155% and 160%, respectively.
机译:本文介绍了一种基于上游集水区的地理和气象特征优化沿海地区粪便指示生物浓度预测模型的方法。从2012年至2013年从巴西南部50个地点采样的贻贝和水中的FIO浓度用于开发模型,以分别预测FIO的时空变化。 FIOs空间变化的预测模型中使用的地理参数是人口,城市地区,不透水覆盖率和集水区总面积。代表距监测点3.1公里以内的流域的模型的R-2比最近的流域高150%。考虑气象参数和不同时间窗口的综合影响,对FIO的时间变化建模。基于降雨和太阳辐射的模型中解释的方差分别增加了155%和160%。

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