Environ Monit Assess (2021) 193:319 https://doi.org/10.1007/s10661-021-09102-1 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 · 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 Abstract  A rational water quality assessment program directly affects a success of a national socioeconomic development strategy This study was aimed to evaluate and classify surface water quality in Dong Thap province, Vietnam, using set pair analysis (SPA) and national water quality index (WQI_ VN) methods The water quality data was collected at 58 locations in 2019 by the Department of Natural Resources and Environment of Dong Thap province 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 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), chlo(SO42−, mg/L), coliform ride ­(Cl−, mg/L), sulfate ­ (MPN/100 mL), and Escherichia coli (MPN/100 mL) were monitored four times a year (58 water samples × 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 deteriorated water quality was in the order of microbiological > 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 quality was evaluated at medium (level III) and poor (level IV) by SPA and WQI_VN, respectively; however, 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 better water 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 potential 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 planning 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) following Vietnam Environment Administration guidelines Vol.:(0123456789) 13 319   Page of 16 (Hung & Nhi, 2018) However, previous studies have reported that water quality has been widely fluctuated among the provinces that depended on the land uses (Giao, 2020a, b; Ly & Giao, 2018; Phung et al., 2015) Therefore, the WQI application for all provinces may not be highly reliable and unable to reflect the quality characteristics of every locality because the importance of the observed water quality parameters 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 considered in several recent studies (Li et  al., 2010; Amiri et al., 2014; Au et al., 2017; Singh et al., 2019) However, 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 quality 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, agriculture, 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, integrating 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 connection numbers is that different methods can give different 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 disciplines 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) Nevertheless, according to Su et al (2020) SPA was mainly used in water resource assessment and was rarely 13 Environ Monit Assess (2021) 193:319 used in water resource analysis, decision-making, classification, and prediction However, some studies have combined this method and traditional calculations in order to be able to deal with the inconsistency 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 comprehensive 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 addition, 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 construction 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 simultaneous application of the water quality index classifications in the current study is essential to be able to test and evaluate water quality most objectively In addition, it could provide additional scientific justification 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 calculation method of Vietnam (WQI_VN) using Dong Thap water quality monitoring data in 2019 The research results provide additional choices in assessing and decentralizing 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 lowest area in the Vietnamese Mekong Delta with the Environ Monit Assess Page of 16  (2021) 193:319 natural area of 3383.85 k­ m2 accounting for 8.17% of the delta area The population is about 1598.8 thousand 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 monsoons 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 humidity 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 average 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 province 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 agricultureforestry-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 billion VND) in 2017 to 6.47% (53,486 billion 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 319 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 program of Dong Thap province in 2019, 58 sampling locations 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 collection (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 samples were collected in one observation (58 samples × 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 Vietnam 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 56676:2014)—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, America; 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 methods (APHA, 1998) Generally, the water quality datasets 13 319   Page of 16 Environ Monit Assess (2021) 193:319 Fig. 1  Map of sampling locations constructed 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 significance 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 13 water quality and its classification or ranking water quality by calculating the degree of linkage (similarity) 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 interconnection, 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: Environ Monit Assess Page of 16  (2021) 193:319 each water quality parameter in set A This weight factor was determined by a variety of methods, such as fuzzy mathematics, analytic hierarchy process, integrated K-means clustering, and entropy weight However, in Vietnam, the entropy information 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) 𝜇 = a + bi + cj 319 where µ is the connection coefficients of the two elements; 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 quality parameters, and set B was formed from five levels based on Decision 1460/QD-TCMT dated November 12, 2019, of the Vietnam Environment Administration on the calculation and publication of the Vietnam Water Quality Index (WQI_VN) (Vietnam Environment 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 corresponding 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 influence can be considered insignificant (Li et al., 2011) The classification of the water quality levels was performed by calculating the weighting factor of Forming the standardization matrix (Xij) 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) Xij of the water quality variable I at the monitoring site j were standardized using Eq. 2: Xij = Cij − Cij (2) Cij max − Cij where Cij was the concentration of water variable i at the monitoring site j Identification of the information entropy values (Hi) by Eqs. 3 and 4: Hi = − � � ∑n fij ln fij j=1 ln n (3) where: Table 1  Hierarchical values of water quality in Vietnam Classifying water quality I II III IV V Ranking concentrations of the calculated water quality variables N-NH4+ N-NO2− N-NO3− P-PO43− pH % DO saturation BOD5 - % mg/L 6.5–7.5 6–6.5 7.5–8 5–6 8–9 4.5–5 9–9.5   9.5 88—112 75—88  ≤ 4  ≤ 10 15   200 COD Coliform E coli MPN/100 mL 13 319   Page of 16 Xij fij = ∑n X j=1 ij Environ Monit Assess (4) It is generally assumed that when fij = 0 or fij = 1, fij was calculated by Eq. 5: + Xij fij = ∑n � � j=1 + Xij (5) The entropy (wi) was obtained by Eqs. 6 and 7: wi = − Hi ∑m m − i=1 Hi (6) The calculated entropy wi should satisfy the conditions of wi ∈ [0, 1] and ∑n w =1 (7) i=1 i 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 ⎧ < Ci ≤ Si1 ⎪ 2×(Ci −Si1 ) Si1 < Ci ≤ Si2 𝜇i1 = ⎨ + Si1 −Si2 ⎪ Ci > Si2 −1 ⎩ ⎧ + 2×(Ci −Si1 ) < Ci ≤ Si1 Si1 −0 ⎪ ⎪ Si1 < Ci ≤ Si2 𝜇i2 = ⎨ 2×(Ci −Si2 ) S < C ≤ S i2 i i3 ⎪ + S −S i2 i3 Ci > Si3 ⎪ −1 ⎩ ⎧ + 2×(Ci −Si2 ) Si1 < Ci ≤ Si2 Si2 −Si1 ⎪ ⎪ Si2 < Ci ≤ Si3 𝜇i3 = ⎨ 2×(Ci −Si3 ) Si3 < Ci ≤ Si4 ⎪ + S −S i3 i4 Ci > Si4 or < Ci ≤ Si1 ⎪ −1 ⎩ ⎧ + 2×(Ci −Si3 ) Si2 < Ci ≤ Si3 Si3 −Si2 ⎪ ⎪ Si3 < Ci ≤ Si4 𝜇i4 = ⎨ 2×(Ci −Si4 ) Si4 < Ci ≤ Si5 ⎪ + S −S i4 i5 C > Si4 or < Ci ≤ Si2 ⎪ i −1 ⎩ 13 (8) (9) (10) (2021) 193:319 ⎧ −1 < Ci ≤ Si3 ⎪ 2×(Ci −Si4 ) Si3 < Ci ≤ Si4 𝜇i5 = ⎨ + Si4 −Si3 ⎪ Si4 < Ci < Si5 ⎩ (12) where i is water quality variable in set A; µi1, µi2, µi3, µi4, and µi5 are the connection degree between i and levels of water quality including the levels of I, II, III, IV, and V, respectively; Ci is the concentration of water quality variable i Si1, Si2, Si3, Si4, and Si5 are the ranking concentration 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 quality levels were performed using %DO saturation The conversion from DO to %DO saturation was made using Eqs. 13 and 14: DOsat = 14.652 − 0.41022T + 0.0079910T − 0.000077774T (13) DO%sat = DO × 100 DOsat (14) where T is the water temperature at the sampling time (°C), and DO is the dissolved oxygen at the monitoring 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: ∑m 𝜇mean = i=1 wi 𝜇ij (15) where µmean is the average connection degree at j, wi is the entropy weight, and µij is the connection degree between every water quality variable to the overall water quality levels Based on the average connection degrees, the classification of water quality level at the monitoring sites was obtained using Eq. 16: ) ( Rj = max 𝜇mean (16) Calculation of Vietnamese water quality index (11) The study calculated the water quality index (WQI_ VN) by converting the water quality parameters using in the equation into individual WQI calculations (Fig. 2) based on the formula (Eq. 17) guided in Environ Monit Assess Page of 16  (2021) 193:319 319 Fig. 2  Calculation steps for water quality index currently used in Vietnam Decision 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 calculation was performed using Microsoft Excel 2016 WQIVN = )1∕2 WQII ( k (17) × Σi=1 WQIII × WQIIII 100 k 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 classification using SPA and WQI_VN The monitoring positions were determined by GPS; then, the coordinates of these locations were added to the software with the data type point The water quality level corresponding to each location was added to the attribute data sheet (numeric) and represented the values in a hierarchical 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 represented 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 autocorrelation (Moran’s I index) was used to determine the extent of spatial dependence of values in water 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 conducted 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 parameters at 58 locations was shown in Fig. 3 The KolmogorovSmirnov 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 normal 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 permissible levels for natural surface water quality regulated in QCVN 08-MT: 2015/BTNMT, column A1 (MONRE, 2015) There were about 20% of the sampling locations with N-NO3− concentration exceeded the acceptable limit for surface water quality In general, organic matters, nutrients, and microorganisms were 13 319   Page of 16 13 Environ Monit Assess (2021) 193:319 Environ Monit Assess (2021) 193:319 ◂ Fig. 3  Q-Q plots for water quality variables in Dong Thap province in 2019 the 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  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, b; 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 fluctuation of COD among the sampling locations It is attributed to the distinct effects of various socioeconomic activities at each location such as industrial zone, market, aquacultural, agricultural, residential areas, and forestry Moreover, the fluctuation of the monitoring times (seasonal variation) can also cause the difference between the monitoring locations because the results of the water quality assessment were calculated the average of the four sampling times Nitrogen and phosphorus compounds are considered 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 development 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 Page of 16  319 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− concentration was significant variation among the locations (p  coliforms > P-PO43− > DO > pH, BOD, COD, and N-NO3− > N-NH4+ Moreover, the (2021) 193:319 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 agricultural 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, improvement policies can be constructed based on the importance of parameters by reducing the density of microorganisms, organic matter, and nutrients Besides, all pollution sources surrounding should be systematically 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 coefficients of 58 locations were then determined by the SPA method combined with the previously calculated 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 presented 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 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 sampling location compared to the other sites For the Table 2  Entropy values of the water quality parameters Values pH DO BOD5 COD N-NO3− N-NO2− N-NH4+ P-PO43− Coliforms E coli Information entropy values (Hi) Entropy weight (wi) 0.98 0.97 0.98 0.96 0.98 0.92 0.99 0.93 0.93 0.85 0.04 0.05 0.04 0.08 0.04 0.16 0.02 0.13 0.14 0.30 13 Environ Monit Assess Page 11 of 16  (2021) 193:319 319 Table 3  Connection coefficients and ranking water quality using SPA and WQI Site Connection coefficients (SPA) S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 Rank WQI Rank Site Connection coefficients (SPA) I II III IV V −0.78 −0.54 −0.84 −0.85 −0.84 −0.81 −0.60 −0.84 −0.84 −0.66 −0.41 −0.25 −0.81 −0.80 −0.80 −0.77 −0.79 −0.23 −0.52 −0.23 −0.27 −0.62 −0.58 −0.73 −0.66 −0.71 −0.77 −0.59 −0.75 −0.11 0.03 −0.12 −0.38 −0.11 −0.07 0.04 −0.41 −0.20 0.05 −0.04 −0.25 −0.23 −0.20 −0.21 −0.22 0.04 −0.27 −0.46 −0.01 −0.01 0.02 −0.04 −0.03 0.02 0.05 −0.03 0.06 0.06 0.03 −0.22 0.09 0.08 0.09 0.05 −0.16 −0.05 −0.06 −0.09 −0.02 −0.40 −0.09 −0.24 −0.23 −0.26 0.04 −0.37 −0.13 −0.20 −0.16 −0.14 −0.19 −0.02 −0.11 −0.04 0.02 −0.16 −0.01 −0.48 −0.67 −0.49 −0.22 −0.48 −0.55 −0.65 −0.21 −0.41 −0.66 −0.55 −0.35 −0.38 −0.69 −0.67 −0.66 −0.63 −0.43 −0.15 −0.61 −0.58 −0.63 −0.58 −0.58 −0.61 −0.64 −0.59 −0.68 −0.69 −0.08 −0.08 −0.08 −0.08 −0.08 −0.08 −0.08 0.05 0.06 −0.08 −0.41 −0.19 0.06 0.20 0.20 0.20 −0.08 −0.24 −0.19 −0.41 −0.41 −0.08 −0.07 −0.08 −0.06 −0.08 −0.08 −0.08 −0.08 III II III III III III II V V II III V V V V V II I IV II II II V III V II III II III 47 58 51 43 49 48 56 44 49 52 59 60 50 25 25 25 53 62 46 64 62 52 51 50 51 52 50 57 52 IV III III IV IV IV III IV IV III III III IV V V V III III IV III III III III IV III III IV III III water quality index, the calculated WQI_VN values at 58 locations ranged from 24 to 64, corresponding to water quality from level III to level V (Table 3) In which, the WQI_VN index of water quality classification at level III occupied 43% (25/58 locations), level IV took 41% (24/58 locations), and level V was 16% Very good water quality level (level I) has not been found using WQI_VN calculation The mean WQI_ VN was at 47 corresponding to water quality class IV There was a difference in the water quality ranks using the SPA and WQI_VN because the weights of their parameters were different The water quality classification using the SPA method combining with entropy weights tended to be better than using S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S57 S58 Rank WQI Rank I II III IV V −0.81 −0.84 −0.84 −0.84 −0.61 −0.67 −0.72 −0.69 −0.84 −0.84 −0.80 −0.84 −0.85 −0.84 −0.84 −0.74 −0.85 −0.62 −0.82 −0.72 −0.73 −0.65 −0.71 −0.75 −0.83 −0.70 −0.77 −0.65 −0.60 −0.16 −0.26 −0.24 −0.05 0.06 −0.03 −0.24 −0.01 −0.45 −0.48 −0.52 −0.27 −0.47 −0.41 −0.25 −0.05 −0.01 0.08 −0.05 −0.08 −0.12 −0.14 −0.31 −0.34 −0.23 −0.28 −0.32 −0.16 −0.05 0.05 0.07 −0.21 0.08 −0.15 −0.08 −0.06 −0.07 −0.06 −0.20 −0.32 0.09 −0.31 0.07 −0.20 −0.02 0.08 −0.14 0.06 −0.03 −0.03 −0.10 −0.33 −0.29 0.08 −0.07 −0.27 −0.11 −0.16 −0.43 −0.34 −0.63 −0.57 −0.69 −0.59 −0.38 −0.60 −0.16 −0.40 −0.09 −0.34 −0.13 −0.20 −0.64 −0.54 −0.59 −0.68 −0.56 −0.54 −0.49 −0.55 −0.65 −0.62 −0.43 −0.39 −0.65 −0.53 −0.63 −0.08 −0.07 0.21 −0.08 −0.08 −0.08 −0.05 −0.07 0.07 0.20 0.28 −0.08 0.33 −0.07 0.20 −0.08 −0.07 −0.08 −0.07 −0.08 −0.07 −0.08 0.20 0.21 −0.08 −0.07 0.20 −0.08 −0.08 III III V III II II V II V V V III V III V III III II III III III V V V III III V V II 44 43 25 51 56 55 53 55 43 24 33 45 36 44 25 54 50 57 49 48 47 50 25 25 47 45 25 49 53 IV III V III III III III III IV V IV IV IV IV V III IV III IV IV IV IV V V IV IV V IV III the WQI_VN at most of the monitoring locations For instance, the water quality classification results at the locations S8, S9, S12, S13, S38, S40, and S42 were classified as level V according to the SPA method, but these sites were classified at the levels IV, IV, III, IV, III, IV, IV, and IV according to WQI_VN index calculation method, respectively The reason of this difference may be the increased importance of certain parameters in the calculation methods (Li et al., 2010) For example, if one monitoring site had high coliform density and high coliform, low water quality (high rank) would be obtained; however, if this coliform density was calculated by the WQI_VN index, the water quality would be assessed at a better level 13 319   Page 12 of 16 because the coliform importance level in the calculation was not completely considered Thus, it can be considered a limitation of the WQI_VN in water quality classification This could mean that the contribution of each water quality parameter has greatly influenced on the methods of the overall water quality assessment Spatial stratification of water quality was developed based on the results of the SPA analysis (Fig. 4a) and the WQI_VN index (Fig. 4b) The SPA hierarchy map indicated that water quality in the northern and southern areas of the Tien River in Dong Thap province was more polluted than that in the midstream area In addition, water quality in canals/rivers in the eastern coastal area was identified to be more polluted than that in the inner parts of the province In contrast, the WQI map hardly clearly delineates the water properties in the parts of Dong Thap province As can be seen in Fig. 4b, the ranking of water quality was not distributed under a certain rule as in Environ Monit Assess map using SPA; this can be demonstrated by spatial autocorrelation (Moran’s I) (Table  4) Specifically, Table  showed that most of the spatial autocorrelation were observed in the SPA ratings, revealing the considerable spatial classification of these water quality index into high and low-value zones Meanwhile, WQI for rank II showed random variation through spatial, which was reflected by weak spatial autocorrelation (−0.125) This variation can be explained by external influences and local sources of pollution In stark contrast, Moran’s I values for rank III and IV of the WQI were 0.874 (z-value = 10.401), and 0.263 (z-value = 2.599) illustrated that the locations adjacent to each other exhibit similar levels of water quality Thereby, as can be seen that the worst water quality was observed in the northern and southern regions of Dong Thap province which was similarly seen in the map using SPA The difference in water quality among the observed locations could be explained by issues related to land use planning—focusing on Fig. 4  Spatial distribution of water quality classified following a SPA method and b WQI 13 (2021) 193:319 Environ Monit Assess Page 13 of 16  (2021) 193:319 Table 4  Spatial autocorrelation of water quality indices (Moran’s I) WQI_VN Moran’s I z-Core p-Value Moran’s I z-Core p-Value II III IV WQI −0.125 0.047 0.356 SPA II 0.098 0.955 0.172 0.874 10.401 0.001 0.263 2.599 0.002 0.975 15.712 0.001 III 0.859 8.398 0.001 V 0.671 7.616 0.001 SPA 0.940 14.835 0.001 Water quality index with a larger Moran’s I means its spatial autocorrelation is more significant agricultural development in the north; industry and services in the southern and forestry in the remote areas of Dong Thap province (Socialist republic of Vietnam Government, 2018) Typically, catfish cage culture (Pangasius catfish) has mostly been recorded concentrated along Tien River in the north of Dong Thap (Phu & Yi, 2005) The feed of the catfish is usually low in protein; therefore, the content of excess nutrients is low However, organic matter, suspended matter can lead to humus accumulation and high biological oxygen demand (Phu & Yi, 2005) Besides that, the declining trends and erratic fluctuations in upstream water resources can lead to changes in water flow, water levels, and sediment in the water, which cause negative changes to water quality (Hoang et al., 2019) At the same time, with the decline in the amount of sediment entering the agricultural cultivation areas, the soil is not provided with nutrients and sanitation; this has caused the soil to become increasingly degraded This is one of the reasons leading to the increase in the amounts of fertilizers and pesticides in rice cultivation (Chapman et al., 2016; Phung et al., 2017) This can be considered one of the main causes of the deterioration of water quality in the North In general, Moran’s I values for WQI and SPA indices have shown the spatial autocorrelation of the overall and different levels in the study area The results of GIS-based water quality analysis showed that water quality in the remote areas of Dong Thap province tended to be slightly polluted compared to suburban areas where the water quality is usually 319 disturbed by large rivers, and water input from other areas outside Dong Thap province After flowing through Dong Thap province, surface water quality is still at good level (based on SPA method) and medium level (based on WQI_VN method) before entering Tien Giang province and Can Tho city which could be the role of natural wetlands here Conclusion The concentrations of TSS, BOD, COD, N-NH4+, N-NO2−, P-PO43−, coliform, and E coli exceeded the permitted limits of natural surface water quality regulated in QCVN 08-MT: 2015/BTNMT, column A1 The means of entropy calculation showed that the serious pollution problems were in the order of microbiological pollution > nutrient pollution > organic matter pollution Specifically, the results of entropy analysis depict E coli, N-NO2−, coliforms, P-PO43−, and DO as the most contributing parameters influencing the water quality, and much emphasis should be paid on these parameters to stop further water pollution While water quality in Dong Thap province was ranked at level III (medium) based on the SPA method, this water quality rank was at level IV (poor) using the WQI_VN method, which were suitable for irrigation and other equivalent purposes The combination of the SPA method and entropy weight can give better performance for water quality assessment compared to the WQI_VN There was a difference in water quality distribution between sampling locations that can be attributed to various land uses Spatial autocorrelation for each water quality rank of SPA is higher than that of WQI; this was contrasted the overall autocorrelation The spatial distribution maps demonstrate that the northern and southern areas of the Tien River and the canals in the eastern coastal area in Dong Thap province were seriously more polluted The set pair analysis can be a promising and effective tool in 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