Where, Qi = sub-index for ith water quality parameter; Wi = weight associated with ith water quality parameter; n = number of
water quality parameters; and Ci = the value assigned to parameter i after normalization (
TuranKoÇer and Sevgili 2014
). The
weights for NSFWQI and WQI
min
are been extracted from
Brown et al. (1970)
and
Pesce and Wunderlin (2000)
, respectively.
2.4. Statistical analysis
At
first, the seasonal data were classified according to spring, summer, autumn and winter. The seasonal average flow rate of the
river during the studying period were 4.1, 6.3, 1.2 and 1.5 m3/s, respectively, at the SH3 station. To identify the factors or sources
which were responsible for water quality variations, PCA was applied. Through
field measurements,
Olsen et al. (2012)
demonstrated
that PCA had the advantage over other methods that it can provide a better recognition of e
ffective pollutant factors across river
reaches.
PCA derives information on the most signi
ficant quality parameters due to spatial and seasonal variations (
Singh et al., 2004
). For
this purpose, the normality of data was tested using the Shapiro-Wilk test for each parameter. The water quality data with non-normal
distribution were logarithmically transformed. The loaded parameters that were the main gradients of PCA were selected for WQI
min-
p
calculations. The correlation matrix was used to check the relationship between water quality parameters and the resulting index
scores. The results of the analysis of variance were used in Minitab and SPSS to check for signi
ficant differences between the WQI
scores of the sampling sites. Additionally, multiple comparisons between factors (i.e. sites) were performed by Duncan test.
3. Results and discussion
3.1. Spatial and seasonal discharge trends of the shahr chai river
The magnitude of water quality changes depends on river regime, catchment characteristics and human activities which are
system speci
fic (
Van Vliet and Zwolsman 2008
). The Shahr Chai
flow rate increased at SH2 due to tributary branches growth.
Continuing along the river, the
flow rate decreased because of increasing agricultural water consumption. During all seasons except
for the spring, the minimum
flow rate was measured at SH6 (the last station before Lake Urmia) (
Fig. 2
). The SH3
flow rates (in the
reservoir outlet) were 43%, 300%, 67% and 56% of SH1 station, during spring, summer, autumn and winter, respectively. The SH6
flow rates were decreased 35%, 69% and 76% in comparison with SH3 during spring, autumn and winter, respectively. Athwart
winter season, there was no signi
ficant difference between the SH3 and the SH6 flow rates in spring and autumn. The summer months
experienced a signi
ficant difference in the stations’ flow rates (in the range of 0.6–6.3 m
3
/s) as a result of the dam water release
schedules (SH3) and increasing agricultural water use in downstream of the river. A dry status of the Shahr Chai River was reported in
SH5 and SH6 stations in August in the course of the study period.
3.2. Spatial and temporal variations of water quality parameters
The river
’s seasonal behavior caused tremendous variations in water quality parameters. The TN and TP parameters did not have
Fig. 2. Seasonal
flow rates of the Shahr Chai River.
K. Zeinalzadeh, E. Rezaei
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