利用高频传感器数据理解淡水溪流中水质变量与硝酸盐浓度的关联
Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data
AI总结：
本研究利用美国国家生态观测网络三个不同站点的高频水质数据，通过广义加性混合模型等方法分析硝酸盐与多类水质变量的非线性关系，证实可用同一组变量构建适用于不同环境的硝酸盐模型，为低成本水质监测提供支撑。
摘要：
利用原位传感器进行实时监测正成为流域内水质测量的常用方法。高频测量产生的大数据集为开展新分析提供了机遇，有助于加深对水质动态的理解，更有效地管理河流与溪流。其中最为重要的是增进对硝酸盐（水生环境中最具反应活性的无机氮形态之一）与其他水质变量之间关系的认知。我们分析了美国国家生态观测网络（National Ecological Observatory Network）中位于不同流域和气候带的三个站点的原位传感器高频水质数据。我们采用广义加性混合模型（generalised additive mixed models），阐释每个站点的硝酸盐浓度与电导率、浊度、溶解氧、水温及海拔之间的非线性关系。通过自回归移动平均模型（auto regressive moving average model）对时间自相关性进行建模，并考察了各解释变量的相对重要性。所有站点的模型解释的总偏差均较高。尽管不同站点的变量重要性和平滑回归参数存在差异，但对硝酸盐变异解释度最高的模型都包含相同的解释变量。本研究表明，即使是环境和气候特征差异极大的站点，使用同一组水质解释变量构建硝酸盐模型也是可行的。当管理目标是在时空层面深入了解硝酸盐动态并相应调整管理方案时，应用此类模型将有助于管理者选择具有成本效益的水质监测变量。
英文摘要：
Real time monitoring using in situ sensors is becoming a common approach for measuring water quality within watersheds. High frequency measurements produce big data sets that present opportunities to conduct new analyses for improved understanding of water quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water quality variables. We analysed high frequency water quality data from in situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto correlation was modelled with an auto regressive moving average model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites. Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost effective water quality variables to monitor when the goals are to gain a spatially and temporally in depth understanding of nitrate dynamics and adapt management plans accordingly.
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