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Geostatistical Mapping of Outfall Plume Dispersion Data gathered with an autonomous underwater vehicle

机译:用自动水下航行器收集的排水管羽流扩散数据的地统计图

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摘要

The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron’s classical estimator was used the compute the experimental semivariogram which was fitted to three theoretical models: spherical, exponential and gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the near by beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume’s dilution are rare, these studies might be very helpful in the future for validation of dispersion models.
机译:这项研究的主要目的是检验地统计学模型的适用性,以获得有价值的信息,以评估污水排污口的环境影响。使用的数据集是通过AUV在对葡萄牙西海岸阿威罗地区附近的S. Jacinto排污口进行的监测活动中获得的。使用Matheron的经典估计量来计算实验半变异函数,该半变异函数适用于三种理论模型:球面,指数和高斯。交叉验证程序建议使用最佳半变异函数模型,并使用普通克里金法获得未知位置的盐度预测。生成的地图清楚地显示了研究区域中的羽流散布,表明流出物没有到达海滩附近。我们的研究表明,从地统计预测的角度出发,针对AUV采样轨迹的最佳设计可以帮助计算更精确的预测,从而更准确地量化稀释度。此外,由于很少进行羽流稀释度的准确测量,因此这些研究对于将来验证分散模型可能非常有帮助。

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