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In this study deterministic, multivariate and stochastic methods are applied to a combined temporal and spatial monitoring data set, in order to assess nitrate and pesticide levels and contamination risk in shallow groundwater.
A field monitoring data set collected on the Erqi Yangtze River Bridge, a three-tower cable-stayed bridge located in Wuhan, China, was used to validate the effectiveness of the proposed integration processing scheme.
We used a large monitoring data set on benthic invertebrates, habitat descriptors, and physico-chemical variables to develop the SPEARhabitat metric that indicates the effects of in-stream habitat degradation.
The proper variables are selected in two stages using a multi-objective optimization approach: in the first stage, a specially designed genetic algorithm minimizes the prediction error over a monitoring data set, while in the second stage a simulated annealing technique aims at the reduction of the number of explanatory variables.
This allows an assessment of deficiencies in the monitoring data set produced by the ERN.
To estimate the parameter ε, we calculated the growth rate for each year, at the 7 locations using the monitoring data set.
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However, when for each pair of monitoring stations in our observed monitor data set we plotted the absolute difference in site means against distance there was no evidence of a linear relationship whether for loge(NO2) or ozone, urban or rural.
This paper defines and explores the use of eco-epidemiological analysis of surveillance monitoring data sets via a proxy to quantify mixture impacts on ecosystems.
It uses web log processing and network traffic monitoring data sets as streaming data sources.
Additional sensor devices such as additional motion detectors, access contacts on the refrigerator or doors, a smart pillbox, or IP cameras can be utilized to supplement the monitoring data sets being produced within the home.
Regression equations and slopes between the two monitoring data sets were statistically indistinguishable.
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