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利用传感器网络监测萨凡纳河溶解氧和水温度的时空变化。

Monitoring spatial and temporal variation of dissolved oxygen and water temperature in the Savannah River using a sensor network.

机构信息

Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC, 29634, USA.

Department of Mathematical Sciences, Clemson University, Clemson, SC, 29634, USA.

出版信息

Environ Monit Assess. 2018 Apr 10;190(5):272. doi: 10.1007/s10661-018-6646-y.

Abstract

Dissolved oxygen is a critical component of river water quality. This study investigated average weekly dissolved oxygen (AWDO) and average weekly water temperature (AWT) in the Savannah River during 2015 and 2016 using data from the Intelligent River sensor network. Weekly data and seasonal summary statistics revealed distinct seasonal patterns that impact both AWDO and AWT regardless of location along the river. Within seasons, spatial patterns of AWDO and AWT along the river are also evident. Linear mixed effects models indicate that AWT and low and high river flow conditions had a significant impact on AWDO, but added little predictive information to the models. Low and high river flow conditions had a significant impact on AWT, but also added little predictive information to the models. Spatial linear mixed effects models yielded parameter estimates that were effectively the same as non-spatial linear mixed effects models. However, components of variance from spatial linear mixed effects models indicate that 23-32% of the total variance in AWDO and that 12-18% of total variance in AWT can be apportioned to the effect of spatial covariance. These results indicate that location, week, and flow-directional spatial relationships are critically important considerations for investigating relationships between space- and time-varying water quality metrics.

摘要

溶解氧是河水水质的一个关键组成部分。本研究利用智能河流传感器网络的数据,调查了 2015 年和 2016 年萨凡纳河的平均每周溶解氧(AWDO)和平均每周水温(AWT)。每周数据和季节性汇总统计揭示了无论沿河流的位置如何,都会对 AWDO 和 AWT 产生影响的明显季节性模式。在季节性内,河流沿线 AWDO 和 AWT 的空间模式也很明显。线性混合效应模型表明,AWT 和低、高河流流量条件对 AWDO 有显著影响,但对模型的预测信息贡献不大。低、高河流流量条件对 AWT 有显著影响,但对模型的预测信息贡献也不大。空间线性混合效应模型产生的参数估计与非空间线性混合效应模型基本相同。然而,空间线性混合效应模型的方差分量表明,AWDO 的总方差中有 23-32%可以归因于空间协方差的影响,而 AWT 的总方差中有 12-18%可以归因于空间协方差的影响。这些结果表明,位置、周和流向空间关系是研究时空变化水质指标之间关系的关键考虑因素。

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