Analysis Of Multivariate Observations From A Monitoring Station Of A River Basin
Author(s)
C. Capilla
Abstract
Water quality evaluation provides important information to estimate water
system status and to test for compliance with standards. The present work
analyzes changes concerning the quality of surface water using a data set from a
monitoring station of a Mediterranean river basin. The data set has a dependency
structure that renders it multivariate. Principal component analysis is applied to
characterize associations present in the multivariate measurements. The principal
component scores exhibit temporal correlation. A combined Shewhart-CUSUM
control chart is applied to the residuals of the scores time series model to detect
changes in the mean level of the data set during the study period. This method
detects an outlying observation in the study period due to an extreme value in
magnesium concentration. The multivariate assessment of trend is performed
using non-parametric tests. The covariance inversion test supported rejection of
the hypothesis of no trend in the variables defined with each combination of
water quality parameters and month. There is heterogeneity between the trends in
the different combinations and an overall trend is not representative. The partial
Mann-Kendall test is employed to analyze the trends of each physicochemical
variable in the study months. Conductivity trends in two months (May and June)
are significant and upward. In the same months calcium trend is also significant
but downward. Sodium concentrations exhibit a significant decreasing trend in
April. Magnesium levels significantly decrease in March but have an upward
trend in June.
Keywords
water quality, multivariate observations, statistical analysis, principal component analysis, control chart, non-parametric trend test
Related Book
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