Classification of water quality in low‑lying area in Vietnamese Mekong delta using set pair analysis method and Vietnamese water quality index Nguyen Thanh Giao · Huynh Thi Hong Nhien
Trang 1Classification of water quality in low‑lying area
in Vietnamese Mekong delta using set pair analysis method
and Vietnamese water quality index
Nguyen Thanh Giao · Huynh Thi Hong Nhien · Phan Kim Anh ·
Duong Van Ni
Received: 12 November 2020 / Accepted: 27 April 2021
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
quality was evaluated at medium (level III) and poor (level IV) by SPA and WQI_VN, respectively; how-ever, the combination of SPA and entropy weight was considered more efficient in this classification and a positive spatial autocorrelation was also found through Moran’s I The spatial distribution of water quality based on SPA classification revealed that bet-ter wabet-ter quality was found in the inner parts of the study area Due to its ease and effectiveness, set pair analysis should be considered for inclusion in the water quality assessment program of Vietnam
Keywords Nutrients · Organic matters · Water
quality index · Microbial pollution · Set pair analysis · Dong Thap
Introduction
Water quality monitoring is regularly considered the top priority issue over the world to avoid poten-tial risks to human health (Islam et al., 2020) This
is not only a plan to assess impacts of pollution sources but also to ensure effective water use plan-ning and management for each region In Vietnam, this monitoring program is implemented under the National Monitoring Program and each province has
a different water quality monitoring network Water quality was assessed using single variable or using the calculation of water quality index (WQI) follow-ing Vietnam Environment Administration guidelines
Abstract A rational water quality assessment
pro-gram directly affects a success of a national
socio-economic development strategy This study was
aimed to evaluate and classify surface water quality in
Dong Thap province, Vietnam, using set pair
analy-sis (SPA) and national water quality index (WQI_
VN) methods The water quality data was collected
at 58 locations in 2019 by the Department of
Natu-ral Resources and Environment of Dong Thap
prov-ince Sixteen variables including temperature (°C),
pH, turbidity (NTU), dissolved oxygen (DO, mg/L),
biological oxygen demand (BOD, mg/L), chemical
oxygen demand (COD, mg/L), total suspended
sol-ids (TSS, mg/L), ammonia (N-NH4+, mg/L), nitrite
(N-NO2−), nitrate (N-NO3−, mg/L), total nitrogen
(TN, mg/L), orthophosphate (P-PO43−, mg/L),
chlo-ride (Cl−, mg/L), sulfate (SO42−, mg/L), coliform
(MPN/100 mL), and Escherichia coli (MPN/100 mL)
were monitored four times a year (58 water
sam-ples × 16 parameters × 4 monitoring times) The
findings presented that TSS, BOD, COD, N-NH4+,
N-NO2−, P-PO43−, coliform, and E coli were the
main constraints on water quality The results of
the entropy weight calculation indicated that
dete-riorated water quality was in the order of
microbio-logical > nutrients > organic matters Surface water
N. T. Giao (*) · H. T. H. Nhien · P. K. Anh · D. Van Ni
College of Environment and Natural Resources, Can Tho
University, Can Tho 900000, Vietnam
e-mail: ntgiao@ctu.edu.vn
Trang 2(Hung & Nhi, 2018) However, previous studies have
reported that water quality has been widely
fluctu-ated among the provinces that depended on the land
uses (Giao, 2020a, ; Ly & Giao, 2018; Phung et al.,
2015) Therefore, the WQI application for all
prov-inces may not be highly reliable and unable to reflect
the quality characteristics of every locality because
the importance of the observed water quality
param-eters in this calculation is equal To avoid the bias in
the calculation, each water quality parameter with its
importance (or weighted factor) has also been
consid-ered in several recent studies (Li et al., 2010; Amiri
et al., 2014; Au et al., 2017; Singh et al., 2019)
How-ever, the weight of a discrete set of parameters is not
linked to the water quality standards since the value
of the observed parameters is uncertainty (Li & Liu,
2009; Wang et al., 2009) Therefore, studies for a tool
that can comprehensively evaluate the water
qual-ity for universal use have attracted attention of many
researchers (Islam et al., 2020)
In recent years, set pair analysis (SPA) has been
used extensively in a wide range of disciplines, such
as engineering geology, meteorology, climatology,
atmospheric environmental science, ecology,
agricul-ture, hydrology, and water resources (Su et al., 2019,
2020; Tian & Wu, 2019) Typically, the study of Su
et al (2020) has shown that the number of studies has
increased significantly by bibliometric analysis Set
pair analysis is an improved uncertainty theory,
inte-grating two factors of uncertainty and certainty; this
is described through the degree of connection from
three factors (identity, discrepancy, and contrary)
The advantage of this approach is the relationship of
the connection numbers, which can deal with both
certainty and uncertainty (Su et al., 2019, 2020) In
addition, the theoretical system and the calculation
method are simple and convenient for application
One of the biggest difficulties in determining
connec-tion numbers is that different methods can give
dif-ferent results, which can interfere with the evaluation
(Su et al., 2020) Therefore, the calculation methods
depend on the experience of the researcher and the
characteristics of each area in order to select suitable
methods (Su et al., 2020) Especially, in the
disci-plines of water resources, the SPA method has rapidly
developed through different period (Jia et al., 2015; Li
et al., 2016; Miao et al., 2019; Su et al., 2020)
Never-theless, according to Su et al (2020) SPA was mainly
used in water resource assessment and was rarely
used in water resource analysis, decision-making, classification, and prediction However, some studies have combined this method and traditional calcula-tions in order to be able to deal with the inconsist-ency between the water quality evaluation parameters and the complex nonlinear relationship between the parameters and the national water quality standards
At the same time, SPA was used to classify water quality and is an effective method for the comprehen-sive water quality assessment and classification (Jia
et al., 2015; Zhu et al., 2016; Zou et al., 2006)
With the topographic characteristics in the Mekong delta, the province is less affected by tides and saltwater intrusion than the other coastal areas, but it is influenced by the dam systems from the upstream of the Mekong River (Li et al., 2017; Dinh, 2014; Thieu & Dung, 2014) Therefore, the water quality in this low-lying area can be severely affected
by water source from the upstream of the Mekong River, especially during the flooding season In addi-tion, water quality in Dong Thap province can be directly controlled by the mid- and coastal regions of the Mekong Delta (Turner et al., 2009) The construc-tion of unplanned dikes can obstruct floods, change water currents, and saline intrusion can be prolonged
in the coastal provinces (Van & Son, 2016) leading to changes in surface water quality Therefore, the simul-taneous application of the water quality index classi-fications in the current study is essential to be able
to test and evaluate water quality most objectively
In addition, it could provide additional scientific jus-tification and effectiveness of the methods in future studies However, the SPA approach is not popular in Vietnam This present study was implemented to rank water quality using set pair analysis method (SPA) in comparison with the current water quality index cal-culation method of Vietnam (WQI_VN) using Dong Thap water quality monitoring data in 2019 The research results provide additional choices in assess-ing and decentralizassess-ing water quality in Vietnam
Materials and methods
Study site description Dong Thap province is one of three provinces in the Dong Thap Muoi floodplain which is the low-est area in the Vietnamese Mekong Delta with the
Trang 3natural area of 3383.85 km2 accounting for 8.17% of
the delta area The population is about 1598.8
thou-sand people with the density of 472 people per km2
The climate of the Dong Thap province has tropical,
hot, and humid, greatly influenced by seasonal
mon-soons The annual average temperature of the province
ranged from 26 to 27 °C The average annual rainfall
was up to 1500 mm, and the average relative
humid-ity for many years was 82—83% There is abundant
surface water and freshwater all year round without
being salty However, acidity is the main problem for
the water source in some central areas both in the dry
season and early rainy season Surface water resources
are strongly affected by the Tien River with the
aver-age flow of 11,500 m3/s, and the province has more
than 1000 large and small canals with the density of
1.86 km/km2 The Tien River flows through 10 out
of 12 districts, towns, and cities of Dong Thap
prov-ince with the mainstream length of about 122.9 km
(Department of Agriculture and Rural Development
in Dong Thap Province, 2013) Besides, two branches
of So Thuong and So Ha Rivers from Cambodia flow
parallel to the Tien River into the northern territory of
Dong Thap province The topography is low, and the
province belongs to the regions where the water depth
is relatively high in the flood season (about 3.25 m
high in normal conditions and up to 4.25 m in high
flood conditions) This place is directly influenced
by the hydrological regime of the Mekong River and
is often flooded during the annual flood season (Mai
& Trung, 2017; Van et al., 2018) The topography of
Dong Thap province is divided into two main regions:
(1) the north of Tien River is low-lying area, and this
is the region for economic development in
agriculture-forestry-fisheries; and (2) the south of Tien River is
deposited with alluvium annually by Hau River and
Tien River—this is the area close to the economic
center of the region, thus mainly developing industry,
commerce, and tourism The gross regional domestic
product (GRDP) increased from 6.04% (47,093
bil-lion VND) in 2017 to 6.47% (53,486 bilbil-lion VND)
in 2019, in which agriculture-forestry-fishery reached
about 1.81% (16,514 billion VND) in 2017 and 3.15%
(18,616 billion VND) in 2019; the growth rates of
the local industry and construction reached 7.14%
(10,829 billion VND) and 9.8% (12,506 billion VND)
in 2017, respectively; business trade and services had
a decrease in its average growth rate from 9.23% in
2017 to 7.52% in 2019, but its GRDP value tended
to increase from VND 19,749 billion in 2017) to VND 22,364 billion in 2019 The per capita income was about 37.47 million VND in 2017 and increased
to 50.46 million VND in 2019 (Provincial People’s Council of Dong Thap province, 2017, 2020)
Sample collection and analysis According to the surface water quality monitoring pro-gram of Dong Thap province in 2019, 58 sampling loca-tions were distributed along the Tien River, Hau River, and inland canals (Fig. 1) Water samples were collected
at the depth of 0.3—0.5 m from the water surface, and the central point of the canal was preferred for data col-lection (depending on the width of the river/canal) with the frequency of four times per year (i.e., February, May, August, and November) This means that 58 water sam-ples were collected in one observation (58 samsam-ples × 4 times) In addition, each sampling month represents a different climatic characteristic, specifically February— between the dry seasons, May—the beginning of the rainy season, August—between the rainy seasons, and November—the beginning of the dry season After that, these samples were stored in 1-L plastic bottles at 4 °C and transported to the laboratory of Natural Resources and Environmental Monitoring of Dong Thap province for analysis The samples are collected, preserved, and processed in accordance with the guidance of the Viet-nam Environment Administration in 2016 and 2018, including the following text numbers TCVN 6663-3:
2016 (ISO 5667-3: 2012)—preservation and handling
of water samples; TCVN 6663-6:2018 (ISO 5667-6:2014)—guidance on sampling of rivers and streams Temperature (°C), pH, turbidity (NTU), and dissolved oxygen (DO, mg/L) were in situ measured at the field using handheld meters (pH HQ 11D—Hach, Amer-ica; EZDO TUB 430—Ezdo, Taiwan; DO HANNA HI9146—Hanna, Romania) which were calibrated prior
to use in each sampling time Biological oxygen demand (BOD, mg/L), chemical oxygen demand (COD, mg/L), total suspended solids (TSS, mg/L), ammonia (N-NH4+, mg/L), nitrite (N-NO2−), nitrate (N-NO3−, mg/L), total nitrogen (TN, mg/L), orthophosphate (P-PO43−, mg/L), chloride (Cl−, mg/L), sulfate (SO42−, mg/L), coliform
(MPN/100 mL), and Escherichia coli (MPN/100 mL)
were analyzed at the laboratory using standard meth-ods (APHA, 1998) Generally, the water quality datasets
Trang 4constructed in this study include 58 water samples × 16
parameters × 4 monitoring times
Data analysis
Water quality assessment was performed based on
the mean values of each water quality variable of
four sampling times at 58 locations and presented as
a standard distribution chart (Q-Q plot) to describe
the spatial variation of water quality Because the
number of sampling sites was greater than 50, the
study was used the Kolmogorov-Smirnov test and
considered to have a normal distribution if the
sig-nificance level (Sig.) was greater than 0.05 (p > 0.05)
using SPSS version 20.0.0 (IBM Corp., Armonk,
NY, USA) In addition, the study evaluated surface
water quality and its classification or ranking water quality by calculating the degree of linkage (similar-ity) between the observed samples and water quality regulations based on the set pair analysis (SPA) The study also performed water quality index calculations currently applicable in Vietnam (WQI_VN) for the comparison
Set pair analysis method SPA is used to compare the similarities between two objects These two objects are a system of intercon-nection, restriction, and interaction with each other and forming a pair of certainty and uncertainty sets
C (A, B) The degree of connectedness of the two objects is determined by Eq. 1:
Fig 1 Map of sampling
locations
Trang 5where µ is the connection coefficients of the two
ele-ments; a, b, and c represent the unity, discrepancy,
and contrary of the two elements, respectively; i is the
different coefficient ranging from −1 to 1; and j is the
discriminant factor
In this study, set A was formed by 10 water
qual-ity parameters, and set B was formed from five levels
based on Decision 1460/QD-TCMT dated November
12, 2019, of the Vietnam Environment
Administra-tion on the calculaAdministra-tion and publicaAdministra-tion of the Vietnam
Water Quality Index (WQI_VN) (Vietnam
Environ-ment Administration, 2019) The five water quality
levels were arranged in descending order, including
level I—very good, II—good, III—medium, IV—poor,
and V—very poor water quality Hierarchical values of
water quality parameters are shown in Table 1
According to the study of Li et al (2011), the
degree of connection between Vietnamese regulations
and the importance of each water quality parameter is
different Therefore, the study calculated the weights
of the parameters participating in water quality
assessment/classification Each weighting factor can
represent the role and importance of the
correspond-ing parameter in the comprehensive water quality
assessment (Sijing et al., 2009) The more weighted
parameters are, the greater the effect is, and vice
versa, the smaller the weighted parameter, the
influ-ence can be considered insignificant (Li et al., 2011)
The classification of the water quality levels was
performed by calculating the weighting factor of
(1)
𝜇 = a + bi + cj each water quality parameter in set A This weight
factor was determined by a variety of methods, such as fuzzy mathematics, analytic hierarchy
pro-cess, integrated K-means clustering, and entropy
weight However, in Vietnam, the entropy informa-tion method commonly is used by entropy that can measure the dispersion of the data, the efficiency
of the information provided by the data (Au et al., 2017); the bigger coefficient Hi, the smaller the entropy and the level of influence on water quality (Li et al., 2016) The entropy weight was determined
as follows:
1 Forming the standardization matrix (X ij) for water quality variables and the monitoring sites, where
i is the evaluating water quality parameter (i = 1,
2, 3,…m), and j is the monitoring site (j = 1, 2,
3, …n) X ij of the water quality variable I at the monitoring site j were standardized using Eq. 2:
where Cij was the concentration of water variable
i at the monitoring site j.
2 Identification of the information entropy values
(H i) by Eqs. 3 and 4:
where:
(2)
X ij= C ij − C ij min
C ij max − C ij min
(3)
ln n
∑n
j=1f ij ln�
f ij�
Table 1 Hierarchical values of water quality in Vietnam
Classifying
water quality Ranking concentrations of the calculated water quality variables
Trang 6It is generally assumed that when f ij = 0 or f ij = 1,
f ij was calculated by Eq. 5:
3 The entropy (w i) was obtained by Eqs. 6 and 7:
The calculated entropy w i should satisfy the
con-ditions of w i ∈ [0, 1] and
Then, the calculations of the connection between the
water quality variables in set A and every level of water
quality in set B were obtained by Eqs. 8 12
(4)
f ij= ∑n X ij
j=1X ij
(5)
f ij= ∑n1+ X ij
j=1
�
1+ X ij�
(6)
i=1H i
(7)
∑n
i=1 w i= 1
(8)
𝜇 i1=
⎧
⎪
⎨
⎪
⎩
1
1+ 2×(C i −S i1)
S i1 −S i2
−1
0 < C i≤S i1
S i1 < C i≤S i2
C i > S i2
(9)
𝜇 i2=
⎧
⎪
⎪
⎨
⎪
⎪
⎩
1+ 2×(C i −S i1)
S i1−0
1
1+ 2×(C i −S i2)
S i2 −S i3
−1
0 < C i≤S i1
S i1 < C i≤S i2
S i2 < C i≤S i3
C i > S i3
(10)
𝜇 i3=
⎧
⎪
⎪
⎨
⎪
⎪
⎩
1+ 2×(C i −S i2)
S i2 −S i1
1
1+ 2×(C i −S i3)
S i3 −S i4
−1
S i1 < C i≤S i2
S i2 < C i≤S i3
S i3 < C i≤S i4
C i > S i4 or 0 < C i≤S i1
(11)
𝜇 i4=
⎧
⎪
⎪
⎨
⎪
⎪
⎩
1+ 2×(C i −S i3)
S i3 −S i2
1
1+ 2×(C i −S i4)
S i4 −S i5
−1
S i2 < C i≤S i3
S i3 < C i≤S i4
S i4 < C i≤S i5
C i > S i4 or 0 < C i≤S i2
where i is water quality variable in set A; µ i1 , µ i2 , µ i3,
µ i4 , and µ i5 are the connection degree between i and
levels of water quality including the levels of I, II,
III, IV, and V, respectively; C i is the concentration of
water quality variable i.
S i1 , S i2 , S i3 , S i4 , and S i5 are the ranking
concen-tration of evaluating variable i corresponding to the
water quality levels of I, II, III, IV, and V, respectively The connection levels between DO and water qual-ity levels were performed using %DO saturation The conversion from DO to %DO saturation was made using Eqs. 13 and 14:
where T is the water temperature at the sampling time
(°C), and DO is the dissolved oxygen at the monitor-ing time (mg/L)
The average connection degree of the water quality
at the monitoring sites with the water quality levels was calculated using Eq. 15:
where µmean is the average connection degree at j, w i
is the entropy weight, and µ ij is the connection degree between every water quality variable to the overall water quality levels
Based on the average connection degrees, the clas-sification of water quality level at the monitoring sites was obtained using Eq. 16:
Calculation of Vietnamese water quality index The study calculated the water quality index (WQI_ VN) by converting the water quality parameters using in the equation into individual WQI calcula-tions (Fig. 2) based on the formula (Eq. 17) guided in
(12)
𝜇 i5=
⎧
⎪
⎨
⎪
⎩
−1
1+2×(C i −S i4)
S i4 −S i3
1
0 < C i≤S i3
S i3 < C i≤S i4
S i4 < C i < S i5
(13)
DOsat= 14.652 − 0.41022T + 0.0079910T2− 0.000077774T3
(14)
DO%sat= DO
DOsat × 100
(15)
𝜇mean=∑m
i=1 w i 𝜇 ij
(16)
R j= max(
𝜇mean)
Trang 7Decision 1460/QD-TCMT dated 12 November, 2019,
of the Vietnam Environment Administration on the
issuance of manual for calculating water quality index
(Vietnam Environment Administration, 2019) The
water quality parameters used in WQI calculation
were the same those used in SPA method The
cal-culation was performed using Microsoft Excel 2016
Spatial distribution of water quality
Geographic information system QGIS (version
2.18.28) was used as a tool to comparatively present
the spatial distribution of the water quality
classifica-tion using SPA and WQI_VN The monitoring
posi-tions were determined by GPS; then, the coordinates
of these locations were added to the software with the
data type point The water quality level
correspond-ing to each location was added to the attribute data
sheet (numeric) and represented the values in a
hier-archical format (displaying by the range of values)
In this method, a water quality classification was
made based on the range of values assigned to each
level of water quality These levels were then
repre-sented by different colors based on the regulations
of the Vietnam Environment Administration (2019)
Thus, the main purpose of using the GIS technique
is to prepare the water quality hierarchy maps of the
WQI_VN and SPA methods In addition, spatial
auto-correlation (Moran’s I index) was used to determine
the extent of spatial dependence of values in water
(17) WQIVN= WQII
100 ×
(1
kΣk i=1WQIII× WQIIII
)1∕2
quality classification The autocorrelation is evaluated
in the overall spatial, the spatial relationship of WQI and SPA values at all locations, and local spatial, which means that the correlation of the sites was con-ducted based on the rating of the water quality index For the local form, the locations were classified based
on the water quality rating prior to computation of the
Moran’s I index A positive Moran’s I value indicates
the data clustered spatially while a negative Moran’s
I value indicates dispersion (Islam et al., 2017) If
the Moran’s I value is equal to 0, it indicates a spatial
randomness (Islam et al., 2017) In this study, spatial autocorrelation was performed in the GeoDa software (http:// geoda center github io/) (Anselin et al., 2006)
Results and discussion
Surface water quality in Dong Thap province in 2019 The standard distribution chart of water quality parame-ters at 58 locations was shown in Fig. 3 The Kolmogorov-Smirnov test showed the relationship between measured and expected values of pH, DO, BOD, N-NH4+, N-NO3−, and TN that were on the diagonal line and ensured the
nor-mal distribution (p > 0.05) The water quality parameters
including TSS, BOD, COD, N-NH4+, N-NO2−, P-PO43−,
coliform, and E coli were higher than the
permis-sible levels for natural surface water quality regu-lated in QCVN 08-MT: 2015/BTNMT, column A1 (MONRE, 2015) There were about 20% of the sam-pling locations with N-NO3− concentration exceeded the acceptable limit for surface water quality In general, organic matters, nutrients, and microorganisms were
Fig 2 Calculation steps for water quality index currently used in Vietnam
Trang 9the main problems that have resulted in water quality
deterioration
In the study area, average pH value was 7.25 ± 0.01
and ranged from 7.00 to 7.52 which is the neutral
and optimal range for most of biological growth
Turbidity and TSS values were 48.72 ± 1.17 NTU
and 34.60 ± 1.92 mg/L, respectively, and there was a
significant variation among the monitoring locations
(p < 0.05) The increase in turbidity and TSS
val-ues can be explained by the high content of organic
and inorganic matters found in water (Chen & Chau,
2016) In addition, these values in natural water
sources can be increased by natural processes such
as tidal currents, suspended solid overflows, erosion,
and landslides (Olyaie et al., 2015) The DO value
ranged from 4.73 to 5.55 mg/L with the average
con-tent of 5.13 ± 0.02 mg/L This DO level is sufficient
for the survival and development of aquatic
organ-isms (DO > 3 mg/L) (Badaii et al., 2013; Weerasingle
& Handapangoda, 2019) The concentrations of BOD
and COD ranged from 12.75 to 18.00 mg/L and 20.00
to 25.75 mg/L, with the means of 15.03 ± 0.15 mg/L
and 22.34 ± 0.18 mg/L, respectively These values
were lower than those reported in other provinces
such as An Giang and Can Tho (Giao, 2020a, ; Ly
& Giao, 2018) The results of Kolmogorov-Smirnov
test showed that there was no standard distribution of
the COD parameter (p = 0.004) due to the high
fluc-tuation of COD among the sampling locations It is
attributed to the distinct effects of various
socioeco-nomic activities at each location such as industrial
zone, market, aquacultural, agricultural,
residen-tial areas, and forestry Moreover, the fluctuation of
the monitoring times (seasonal variation) can also
cause the difference between the monitoring
loca-tions because the results of the water quality
assess-ment were calculated the average of the four sampling
times
Nitrogen and phosphorus compounds are
consid-ered primary pollutants which can form secondary
pollutants For instance, if the high concentration of
these compounds existed in the water media, it would
cause eutrophication which directly affects the
devel-opment of aquatic organisms (Yang et al., 2007) In
this study, the concentrations of nitrogen compounds
ranged from 0.29 to 0.45 mg/L of N-NH4+, 0.01 to
0.71 mg/L of N-NO2−, 0.99 to 2.70 mg/L of N-NO3−, and 3.34 to 4.65 mg/L of TN; only N-NO2− con-centration was significant variation among the
loca-tions (p < 0.05) In contrast to the results of BOD and
COD, N-NO3− concentration in the studied rivers was found tending to be higher than that of other prov-inces such as An Giang (0.03—1.76 mg/L) and Can Tho (0.5—1.9 mg/L) (Giao, 2020a, b; Ly & Giao, 2018) N-NO2− concentration was recorded relatively high which can prove that the water environment has been polluted for a long time DO depletion can pro-mote nitrate reduction to nitrite by microorganisms leading to increased N-NO2− in water (WHO, 2011; Yang et al., 2007) Similar to COD, the concentra-tion of N-NO2− was recorded with significant varia-tion between rainy and dry seasons, which may lead
to differences between the locations in the results of water quality assessments in 2019 P-PO43− concen-tration ranged from 0.1 to 0.59 mg/L with the aver-age value of 0.23 ± 0.01 mg/L and had no standard
distribution (p < 0.05) The presence of these nutrient
compounds is the results of the overuse of chemical fertilizers, which can cause the eutrophication (Singh
et al., 2017) According to a report by the Department
of Natural Resources and Environment in Dong Thap province (2016), the surface water quality of Dong Thap province has been polluted due to residues of fertilizers, pesticides, and socioeconomic activities The presence of Cl− and SO42− compounds also influences on salinity of water quality (Williams, 2001) Cl− concentration fluctuated in the range
of 9.08—21.84 mg/L, and SO42− concentration ranged from 16.66 to 36.06 mg/L with the average
of 21.46 ± 0.48 mg/L Figure 3 presented a notable increase in Cl− and SO42− concentrations at some mon-itoring locations which could result in abnormal distri-bution of these parameters by Kolmogorov–Smirnov
test (p < 0.05) The fluctuation of these parameters
was attributed to local activities such as discharge of domestic and livestock wastewater and the over appli-cation of inorganic fertilizers (Singh et al., 2017) rather than salinity influence effects (Smitha & Shivashankar, 2013) Coliform density ranged from 1708 to 25,300
MPN/100 mL while the density of E coli ranged from
407 to 6707 MPN/100 mL Escherichia coli density
exceeded 20.33 to 335.4 times the permitted level The
high densities of coliform and E coli could be because
the high presence of nutrients, organic matters, and inadequate sanitation have resulted in high risk to
Fig 3 Q-Q plots for water quality variables in Dong Thap
province in 2019
◂
Trang 10human health and ecosystems (Ahipathy & Puttaiah,
2006; Liang et al., 2003; Yang et al., 2007) This can
be explained by the coliform density directly affected
by many different waste sources such as point sources
(domestic, industrial) and scattered sources (soil
leach-ing, grazing land) In particular, during the rainy
sea-son, the concentration of pollutants from domestic, soil
washout, and the livestock of grazing and poultry has
increased, which facilitate the spread and growth of
microorganisms; therefore, the variation of microbial
density depends on the seasonal variation This has
also been reported in several water bodies in the past;
the density of microorganisms in the rainy season was
higher than that in the dry season (Barakat et al., 2016;
Gavio et al., 2010)
Ranking water quality using SPA method
There were 10 out of 15 parameters used to calculate
and classify water quality through SPA in cooperate
with entropy weight calculation to confirm the level
of the importance of each parameter in the
classifi-cation Table 2 showed that the entropy weights of
four parameters (i.e., N-NO2−, P-PO43−, coliform, and
E coli) were greater than these of the other
param-eters It indicated that the variation in water
qual-ity in Dong Thap province was greatly influenced
by these parameters (w i > 0.1) (Abadi et al., 2017)
In addition, the entropy weight of COD
param-eter (w i = 0.08) also showed an average effect on
water quality The entropy weights of the remaining
parameters such as pH (w i = 0.04), DO (w i = 0.05),
BOD (w i = 0.04), N-NO3− (w i = 0.04), and N-NH4+
(w i = 0.02) were much smaller than 0.1, so these
parameters were expected to impact little on water
pollution in the study area According to the results of
entropy weight calculation, the important level of the
water quality parameters can be arranged as follows:
E coli > N-NO2− > coliforms > P-PO43− > DO > pH,
BOD, COD, and N-NO3− > N-NH4+ Moreover, the
three main problems of water quality in Dong Thap province that were figured out by the entropy weight values were microbiological, nutrient, and organic contamination Based on the assessment results of water quality and development characteristics of Dong Thap province, domestic wastewater and agri-cultural activities are the two main point and nonpoint pollutants
Therefore, it is necessary to prioritize the cleanup strategies in the comprehensive water quality improvement of the province In particular, improve-ment policies can be constructed based on the impor-tance of parameters by reducing the density of micro-organisms, organic matter, and nutrients Besides, all pollution sources surrounding should be systemati-cally investigated as a basis for proposing measures to reduce negative impacts on water quality Thus, the negative effects of all point and nonpoint pollutants
on water sources can be clearly demonstrated
The pollution levels and the connection coeffi-cients of 58 locations were then determined by the SPA method combined with the previously calcu-lated entropy weights The connection coefficient
of the level III (R = 0.03) was higher than the level
I (R = −0.78), the level II (R = −0.11), the level IV (R = −0.48), and level V (R = −0.08) Water quality at
the monitoring site S1 belonged to level III, and this calculation was repeated for the next monitoring sites The results of water quality classification were pre-sented in Table 3 According to the results of the SPA method, water quality in the Dong Thap province was ranked at level III—medium The polluted locations were at medium level—level III accounted for 22/58 locations (37.9%), good—level II (18/58 locations, 31%), very poor—level V (17/58 position, 29.31%), and 1 position (S18, 0.02%) at very good level (level I) Due to significantly lower coliform density in cooperate with its high entropy weight, there was a significant difference in water quality at the S18 sam-pling location compared to the other sites For the
Table 2 Entropy values of the water quality parameters
Information
entropy
values (H i)
Entropy