Spatial analysis of  surface water quality using multivariate statistical techniques and water quality index: Case study of Binh Duong Province, the largest industrial hub in Southern Vietnam

Spatial analysis of surface water quality using multivariate statistical techniques and water quality index: Case study of Binh Duong Province, the largest industrial hub in Southern Vietnam | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial analysis of surface water quality using multivariate statistical techniques and water quality index: Case study of Binh Duong Province, the largest industrial hub in Southern Vietnam Hoai Ngoc Pham, Tuong Dinh Nguyen, Huyen Thanh Phan, Yen My Nguyen, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4457483/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ensuring high‒quality water supply is essential for both domestic and manufacturing activities, particularly in Binh Duong Province (BDP), situated at the heart of Vietnam's southern key economic region, known for its dense population and numerous industrial parks. In this study, multivariate statistical analysis techniques, including Hierarchical Cluster Analysis (CA) and Principal Component Analysis (PCA), were employed to assess spatial variations in surface water quality (SWQ) along the Sai Gon and Dong Nai Rivers, which are the two primary water bodies in BDP. CA classified the 25 sampling sites into three groups (DN, SGDN1, SGDN2) and three outlying groups (RSG8, RSG10, and RDN7). Groups RDN7 and DN were deemed to have good surface water quality, while RSG8 exhibited moderate SWQ. Conversely, RSG10 and SGDN1 were classified as having bad and moderate surface water quality, respectively. The Kruskal‒Wallis test revealed significant spatial differences in all water quality parameters among the six clusters ( p < 0.05). PCA identified two principal components (PCs) explaining 65.3% of the total variance, highlighting NO 3 − , NO 2 − , NH 4 + , PO 4 3− , COD, BOD 5 , and coliform as major pollution sources in the area. The findings underscore the impact of untreated domestic and industrial sewage on water quality in the Sai Gon and Dong Nai Rivers. This study contributes valuable insights into water quality assessment using multivariate statistical methods and informs the formulation of effective public policies by local governments. Cluster analysis industrial pollution principal component analysis water quality management water resources Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Water is a crucial natural resource essential for various biological and non‒biological activities (Pandey et al., 2022 ). Although abundant on Earth, the majority is saline or brackish and unsuitable for consumption or agriculture (Kamboj & Kamboj, 2019 ). Only around 3% of water is freshwater, with just 1% available for drinking and fulfilling basic needs (Singh & Kamal, 2014 ). However, human activities have caused negative impacts on water sources, notably through the contamination and reduction of surface water (Harding et al., 2019 ; Zhang et al., 2015 ). Water quality deterioration persists in developed countries, while it is a major problem in developing countries in which a substantial amount of sewage is discharged directly into rivers (John et al., 2014 ; Mishra et al., 2017 ). There's a rising global apprehension regarding water availability and its quality, with projections indicating a 20 to 30% surge in water demand by 2050 (Wada et al., 2016 ). Furthermore, variations in the hydrological cycle across different regions and times, coupled with uncertainties associated with climate change, could exacerbate this situation (Fan & Shibata, 2015 ; Putro et al., 2016 ). The decline in the SWQ poses a significant threat to human health and elevates the likelihood of waterborne illnesses. Indeed, the utilization of low‒quality water is a contributing factor to the high mortality rate (8.5%) in Southeast Asia (Dairo et al., 2017 ). In that regard, assessing the quality of surface water has become a critical and essential task in every country. In Vietnam, the government has made considerable efforts to implement a surface water monitoring program through the development of legislative documents like the Environmental Protection Law and the Water Resources Law (Lorenz et al., 2021 ; Nguyen et al., 2022 ). Various parameters are measured in the surface water monitoring program to assess water quality. In‒situ measurements include temperature, pH, turbidity, conductivity, total dissolved solids (TDS), salinity, and dissolved oxygen (DO). Laboratory analysis covers biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonia (NH 4 + ), nitrite (NO 2 − ), nitrate (NO 3 − ), orthophosphate (PO 4 3− ), total petroleum hydrocarbons (TPH), chloride (Cl − ), fluoride (F − ), heavy metals (Hg, As, Cu, Zn, Cr, Ni, Pb, Cd, Fe), organic chlorine pesticides (aldrin, dieldrin, DDTs, heptachlor & heptachlor epoxide, total organic carbon), cyanide (CN − ), phenol, and biological indicators ( Escherichia coli , coliform). Determining monitoring parameters and sites for assessing SWQ depends on budgetary constraints and the nature of pollution sources. According to the Vietnamese Ministry of Natural Resources and Environment guidance, SWQ is assessed by comparing parameter values against the national standard (QCVN 08‒MT:2015/BTNMT) (MONRE, 2015). Moreover, the water quality index (WQI) is computed to categorize surface water quality (VEA, 2019). However, these conventional methods fail to fully exploit all essential information from the monitoring datasets, rendering their results less objective (Nguyen et al., 2022 ). For instance, in the calculation of WQI, assigning weights to parameters or variables to reflect their significance is a critical step that impacts the comprehensive assessment of water quality (Lkr et al., 2020 ). Nonetheless, weight-based indices can suffer from imbalanced sensitivity if certain parameters are strongly or weakly weighted. Additionally, these weights are typically determined based on expert judgments, and any alterations in weight assignments can significantly alter the overall interpretation of water quality. For instance, higher assigned weights may disproportionately influence parameters with lower concentrations, leading to inaccurate interpretations of water quality (Juwana et al., 2022). On the other hand, monitoring studies often yield vast and intricate datasets, making it challenging to interpret water quality due to underlying interrelationships among measured parameters (Marinović Ruždjak & Ruždjak, 2015 ). Univariate statistical analysis (i.e., WQI) struggles to capture this complexity (Le et al., 2017 ). Moreover, insights from univariate methods typically focus on specific pollution types and may not effectively support integrated water quality management. While composite indices like WQI consider multiple parameters, they primarily communicate water body health rather than reveal dataset structure (Alberto et al., 2011). Consequently, crucial information, such as pollution sources, may remain hidden within WQI scores after normalization and weighting (Le et al., 2017 ). Therefore, monitoring programs produce extensive datasets, necessitating effective interpretation methods. Multivariate statistics, particularly techniques like Hierarchical Cluster Analysis (CA) and Principal Components Analysis (PCA), are commonly employed to understand relationships between variables and their significance to the studied issue. CA assists in grouping similar variables into clusters, facilitating analysis. On the other hand, PCA serves as a dimension‒reduction tool, condensing large sets of variables into smaller ones while retaining the essential information (Kılıç & Yücel, 2019 ). Multivariate statistical techniques are extensively utilized in environmental monitoring datasets due to their ability to: (i) accurately reflect the multivariate nature of the system, (ii) handle large datasets effectively, and (iii) detect and quantify multivariate patterns arising from the correlation structure of variables (Kılıç & Yücel, 2019 ). Multivariate statistical analysis has been widely accepted and proven effective for various studies, including identifying water quality status in ecological systems, assessing spatial and temporal variations in surface waters, and pinpointing underlying factors contributing to water pollution (Khaledian et al., 2018 lıç & Yücel, 2019; Pratama et al., 2020 ; de Andrade Costa et al., 2020 ). In the context of Vietnam, several studies have highlighted its utility (Giao et al., 2021 ; Nguyen et al., 2022 ; Pham et al., 2022 ; Le et al., 2023 ). Surface water pollution is a significant environmental issue in Vietnam, especially in the Southeast region, which is crucial for industrial and agricultural growth (Lorenz et al., 2024). Binh Duong Province (BDP), located in the Southeast of Vietnam, is renowned for its bustling industrial landscape. With its strategic position adjacent to Ho Chi Minh City, BDP has become a hub for industrial development, drawing in a multitude of factories and manufacturing plants (BDIZ, 2024). While BDP stands as a symbol of industrial prowess and economic dynamism in Vietnam, it also faces the pressing issue of water pollution due to the concentration of industrial activities (Nguyen et al., 2021 ; Thong et al., 2022 ). In addition, surface water resources in BDP are also at risk from pollutants from outside sources, such as urban areas, residential areas, and areas where trade, service, and production are concentrated in the Southeast region. The main rivers and canals in BDP are directly affected by waste discharges from socio-economic development activities, which leads to the need to assess water quality and propose solutions for protection and preservation. The study aimed to (i) analyze spatial changes in water quality characteristics and update water quality diagnosis using multivariate analysis and WQI and (ii) identify key contributors to pollution and sources affecting water quality in the BDP. Findings will offer valuable insights into water quality assessment through multivariate statistical methods and assist local governments in formulating effective public policies. Materials and methods Study area Binh Duong Province has an area of 2694.43 km 2 , with a population of 2,426,561 (2019). The region is subject to a tropical monsoon climate, with a distinct rainy season from May to November and a dry season from December to April. The annual average precipitation ranges from 1.800 mm to 2.000 mm, of which 80% falls in the rainy season. Situated at the heart of the Vietnamese southern key economic region and bordering Ho Chi Minh City, BDP is Vietnam's foremost manufacturing hub with 38 industrial zones (BDIZ, 2024), leading the nation in attracting industrial FDI and poised to evolve into an innovation-driven region. Binh Duong benefits from its strategic location between the Sai Gon and Dong Nai Rivers, which form an extensive waterways network facilitating domestic and industrial activities. The Dong Nai River, originating from the Lam Vien Plateau, is the largest river in the Southeast region, stretching 635 km in length but only traversing through Tan Uyen District. It serves crucial roles in agriculture, water transport, and fisheries. Meanwhile, the Sai Gon River, spanning 256 km, begins its journey from the mountains of Loc Ninh district in Binh Phuoc province, boasting numerous tributaries, canals, and streams. It flows westward through BDP, with the section from Lai Thieu to Dau Tieng covering a distance of 143 km, characterized by gentle slopes ideal for transportation, agriculture, and fisheries. As it progresses upstream, the river meanders and narrows to 20m, gradually widening to 200m from Dau Tieng to Thu Dau Mot City. However, in the context of economic development, the SWQ in the Sai Gon‒Dong Nai river system has been severely contaminated by a large number of industrial and domestic wastewater pollution sources (Nguyen et al., 2021 ; Thong et al., 2022 ). Water sampling and analysis Data were collected at 25 monitoring stations representing the SWQ of the Sai Gon and Dong Nai Rivers in 2021, as depicted in Fig. 1 . Three stations, SG1‒SG3, are situated along the mainstream of the Sai Gon River, while four locations, DN1‒DN4, are positioned along the mainstream of the Dong Nai River. Ten stations designated as RSG1 to RSG10 are situated on tributaries of the Saigon River, while eight stations denoted as RDN1 to RDN8 are located on the Dong Nai River tributaries. Monthly water sampling was conducted at a depth ranging from 0.3 to 0.5 meters below the water surface, with preference given to the central point of the canal, adjusted as per the river or canal's width. Subsequently, these samples were carefully stored in 1‒liter plastic bottles at 4°C and transported to the laboratory of the Natural Resources and Environmental Monitoring Department of BDP for thorough analysis. The sampling, preservation, and processing procedures followed the guidelines established by the Vietnam Environment Administration, referencing specific technical standards: TCVN 6663‒3:2016 (ISO 5667‒3:2012) for water sample preservation and handling, and TCVN 6663‒6:2018 (ISO 5667‒6:2014) for guidance on river and stream sampling techniques. Various water quality parameters were measured both in situ and at the laboratory. In the field, temperature (°C), pH, turbidity (NTU), and dissolved oxygen (DO, mg/L) were measured using handheld meters (pH HQ 11D‒Hach, America; HANNA HI93703‒Hanna; HI9146‒Hanna, Romania), calibrated before each sampling session. Additionally, parameters including biological oxygen demand (BOD 5 , mg/L), chemical oxygen demand (COD, mg/L), total suspended solids (TSS, mg/L), ammonia (NH 4 + , mg/L), nitrite (NO 2 − , mg/L), nitrate (NO 3 − , mg/L), orthophosphate (PO 4 3− , mg/L), and coliform (MPN/100 mL) were analyzed at the laboratory following standard methods (APHA, 2017 ). The summarized monitoring data is provided in Table 1 . Table 1 Summary of water quality in the Sai Gon and Dong Nai Rivers, passing through Binh Duong Province Parameters Temp. pH DO Turb. NO 3 − NO 2 − NH 4 + PO 4 3− TSS COD BOD 5 Coliform Unit °C mg/L NTU mg/L mg/L mg/L mg/L mg/L mg/L mg/L MPN/100mL Count 300 300 300 300 300 300 300 300 300 300 300 300 Average 29.34 6.53 3.04 49.14 1.29 0.10 4.34 0.33 26.52 23.45 11.01 2288.06 Standard deviation 1.21 0.43 1.69 91.78 1.62 0.22 7.58 0.40 26.34 27.27 12.67 900.68 Standard error 0.07 0.02 0.10 5.30 0.09 0.01 0.44 0.02 1.52 1.57 0.73 52 Minimum 26.40 5.60 0.30 2.00 0.06 0.00 0.02 0.02 5.00 4.00 2.00 1.50 Lower quartile 28.40 6.30 1.45 22.00 0.50 0.01 0.34 0.06 11.00 8.00 4.00 2100 Interquartile range 1.70 0.50 3.20 25.75 1.20 0.08 4.99 0.35 19.75 21.00 9.00 700 Upper quartile 30.10 6.80 4.65 47.75 1.70 0.10 5.33 0.41 30.75 29.00 13.00 2800 Maximum 33.00 7.80 6.82 827.00 18.00 2.60 77.50 2.95 240.50 220.00 101.00 4300 Skewness 0.16 0.31 0.29 7.65 5.89 6.87 4.57 2.41 3.73 3.31 3.37 -0.76 Stnd. skewness 1.15 2.21 2.02 54.07 41.62 48.60 32.32 17.02 26.40 23.43 23.86 -5.41 Kurtosis -0.19 0.37 -1.20 61.10 48.34 63.31 32.33 7.76 21.29 15.19 15.67 1.73 Stnd. kurtosis -0.67 1.31 -4.26 216.03 170.91 223.82 114.31 27.45 75.26 53.71 55.41 6.10 Data analysis The Shapiro‒Wilk test checked for normal data distribution ( p > 0.05), while Levene’s test assessed variance homogeneity ( p > 0.05). Assuming both criteria were met, STATISTICA 7.0 performed one‒way ANOVA. For significant results, Tukey HSD post hoc analysis compared group pairs. Alternatively, the Kruskal‒Wallis test and Bonferroni ranks for all groups were replaced if assumptions weren't met despite data transformation. Spearman rank correlation coefficients ( p < 0.05) explored water quality variable relationships. Cluster Analysis (CA) and Principal Component Analysis (PCA) were utilized to examine the spatial patterns of the SWQ across monitoring sites. To ensure accurate analysis and account for measurement unit discrepancies, raw data underwent z‒score transformation, standardizing mean and variance to 0 and 1, respectively (Singh & Kamal, 2014 ). CA identified similarities in water quality composition among monitoring sites, showcasing high homogeneity within groups and heterogeneity between them (Razmkhah et al., 2010 ). Hierarchical clustering, utilizing Euclidean distance, generated dendrograms representing site similarities (Zhao et al., 2021 ). The SIMPROF technique verified significantly distinct groups. PCA elucidated key factors impacting water quality and potential pollution sources (Kowalkowski et al., 2006 ; Chounlamany et al., 2019). Operating on a correlation matrix, PCA reduced data dimensionality, extracting the contributions of each principal component (PC). Eigenvalues measured the extent to which PCs explained original data, with eigenvectors selected based on the Kaiser‒Guttman criterion, considering only those surpassing the mean eigenvalue (Borcard et al., 2011 ). All calculations and graphics were performed in PRIMER v.6 (Anderson et al., 2008 ). Vietnamese water quality index (VN_WQI) In order to evaluate the comprehensive quality of water, the Water Quality Index (VN_WQI) is computed following guidelines outlined by the Vietnam Environment Administration (VEA, 2019). The formula for calculating VN_WQI is shown as follows: VN_WQI = \(\frac{{WQI}_{pH}}{100}{\left[\frac{1}{7}\sum _{a=1}^{7}{WQI}_{a}\times \frac{1}{2}\sum _{b=1}^{2}{WQI}_{b}\times {WQI}_{c}\right]}^{\frac{1}{3}}\) where WQI a for DO, BOD 5 , COD, N‒NH 4 + , N‒NO 3 − , N‒NO 2 − , P‒PO 4 3− ; WQI b for TSS and turbidity; WQI c for coliform; WQI pH for pH. The VN_WQI ranges from 0 to 100, categorizing water quality into five levels: <10 (heavily polluted, requiring immediate treatment), 10–25 (poor, requiring urgent treatment), 26–50 (bad, for transport and equivalent purposes), 51–75 (moderate, for irrigation and other similar purposes), 76–90 (good, suitable for domestic water supply with necessary treatment), and 91–100 (very good, suitable for direct water supply purposes). Results and Discussions Cluster analysis of spatial changes in water quality The results of cluster analysis with the SIMPROF test showed that the surface water quality at 25 monitoring sites formed three groups and three outlying groups (RSG8, RSG10, and RDN7) (Fig. 2 ). The first group, termed the DN group, comprised stations DN1, DN2, DN3, and DN4, all situated within the Dong Nai River. The second group, designated as the SGDN1 group, encompassed stations located in the tributaries of the Sai Gon and Dong Nai Rivers, specifically including RSG4, RSG7, RSG9, RDN2, and RDN5. The remaining 13 stations formed the third group, denoted as the SGDN2 group. The DN and RDN7 groups displayed the highest mean values of DO and WQI, which were significantly different from the other groups (Fig. 3 ). In the DN group, the mean DO values were 5.37 ± 0.40 mg/L, while in the RDN7 group, they were slightly lower at 5.33 ± 0.36 mg/L. The WQI values were 81.41 ± 12.92 and 76.94 ± 12.24 for the DN and RDN7 groups, respectively. Nutrient indicators such as NO 3 − , NO 2 − , NH 4 + , and PO 4 3− , as well as biochemical indicators like COD, BOD 5 , and coliform, were found to be at their lowest levels in the DN and RDN7 groups compared to the other groups. Specifically, in the DN group, the mean values of NO 3 − , NO 2 − , NH 4 + , PO 4 3− , COD, BOD 5 , and coliform were 0.63 ± 0.42 mg/L, 0.02 ± 0.01 mg/L, 0.29 ± 0.33 mg/L, 0.14 ± 0.27 mg/L, 7.83 ± 3.02 mg/L, 3.87 ± 1.49 mg/L, and 2167.9 ± 781.23 MPN/100mL, respectively. According to the WQI, the surface water quality in both the DN and RDN7 groups was classified as good. Conversely, the RSG8, RSG10, and SGDN1 groups exhibited the lowest levels of DO (ranging from 1.84 to 3.83 mg/L) and WQI (ranging from 54.99 to 65.70) in comparison to the other groups. Based on the WQI, the surface water quality in RSG8 and SGDN1 was assessed as moderate, whereas notably, the RSG10 group exhibited bad water quality. Moreover, the nutrient and biochemical indicators in RSG8 and SGDN1 were significantly elevated. Specifically, the mean values of NO 3 − , NO 2 − , NH 4 + , PO 4 3− , TSS, COD, BOD 5 , and coliform in RSG10 were notably high, with corresponding values of 1.12 ± 0.87 mg/L, 0.04 ± 0.05 mg/L, 26.34 ± 19.11 mg/L, 0.82 ± 0.33 mg/L, 50.92 ± 58.77 mg/L, 96.50 ± 61.33 mg/L, 44.67 ± 29.19 mg/L, and 2733.53 ± 1209.59 MPN/100mL, respectively. Therefore, in the case of RSG10, the DO value was 3.73 times lower than the Vietnamese standard A2 (water for residential use with appropriate treatment, preservation of aquatic plants, or other purposes) (MONRE, 2015), while NH 4 + , TSS, COD, BOD 5 , and PO 4 3− levels were 87.8, 1.69, 6.43, 7.44, and 4.1 times higher than the A2. (MONRE, 2015). Additionally, RSG8 recorded elevated values of NO 3 − and NO 2 − , at 6.79 ± 4.82 mg/L and 0.91 ± 0.65 mg/L, respectively (Fig. 3 ). Moreover, the SGDN2 group demonstrated relatively low mean values of DO, measuring at 2.62 ± 1.32 mg/L, while the WQI indicated an average level (74.06 ± 9.64). Additionally, the mean values of NO 3 − , NO 2 − , NH 4 + , PO 4 3− , COD, BOD 5 , and coliform were relatively high, corresponding to 1.21 ± 0.75 mg/L, 0.09 ± 0.09 mg/L, 2.00 ± 2.06 mg/L, 0.19 ± 0.16 mg/L, 15.85 ± 12.51 mg/L, 7.48 ± 7.48 mg/L, and 2240.61 ± 851.49 MPN/100mL, respectively (Fig. 3 ). The WQI categorized the surface water quality in the SGDN2 group as moderate. Principal component analysis and key variables influencing water quality Table 2 illustrates significant correlations among various water quality parameters ( p < 0.05). Temperature, pH, DO, and turbidity correlated with most other parameters. DO exhibited a strong negative correlation with nutrient parameters (NH 4 + , PO 4 3− ) and biological indicators (COD, BOD 5 , coliform), likely indicating the impact of DO on the dynamics of reactive nitrogen in surface water (Le et al., 2017 ; 2019 ). Elevated levels of nutrients in water could potentially result in decreased DO levels owing to the biological oxidation-reduction processes involving algae and bacteria (Hong et al., 2019 ; de Almeida Fernandes et al., 2020). Additionally, an uptick in organic matter (BOD 5 ) is associated with heightened nutrient concentrations (NH 4 + and NO 3 − ) (Giao et al., 2021 ). Conversely, this increase might lead to elevated respiration and organic matter decomposition, ultimately diminishing the DO concentration and detrimentally impacting water quality. PCA extracted the two most representative PCs accounting for 65.3% of the total variance in the data set of the SWQ. PC1, which accounted for 44.5% of the total variance, was positively correlated with DO and turbidity but negatively correlated with most other parameters. Moreover, PC1 was strong negatively correlated with NH 4 + , COD, BOD 5 , coliform, and PO 4 3− . Therefore, PC1 mainly represented organic and biological pollution. PC2, with 20.7% of the total variance, had positive correlations with temperature, turbidity, NH 4 + , TSS, COD, and BOD 5 , strong and negative correlations with NO 3 − and NO 2 − . Thus, PC2 predominantly represented water pollution of nitrates and nitrites (Table 3 ). The surface water quality of groups RDN7, DN (DN1, DN2, DN3, DN4), SGDN2 (SG1, SG2, SG3, RSG1, RSG2, RSG3, RSG5, RSG6, RDN1, RDN3, RDN4, RDN6, RDN8) exhibited high DO) levels and low concentrations of NO 3 − , NO 2 − , NH 4 + , COD, BOD 5 , coliform, and PO 4 3− , indicating good surface water quality. Group RSG8 displayed relatively high DO levels and low concentrations of NH 4 + , COD, and BOD 5 ; however, due to elevated NO 3 − and NO 2 − levels, it was categorized as having moderate surface water quality. In contrast, groups RSG10, SGDN1 (RSG4, RSG7, RSG9, RDN2, RDN5) were characterized by low DO levels and high concentrations of NO 3 − , NO 2 − , NH 4 + , COD, BOD 5 , coliform, and PO 4 3− , leading to classification as having bad and moderate surface water quality, respectively (Fig. 4 ). Table 2 Spearman rank correlation of 12 water quality variables in Sai Gon and Dong Nai Rivers, Binh Duong Province Temp. pH DO Turb. NO 3 − NO 2 − NH 4 + TSS COD BOD 5 Coliform PO 4 3− Temp. 1 pH 0.115* 1 DO -0.261** 0.002 1 Turb. -0.041 0.098 -0.160** 1 NO 3 − 0.197** -0.061 -0.081 -0.118* 1 NO 2 − 0.022 -0.116* -0.008 -0.068 0.491** 1 NH 4 + 0.168** 0.129* -0.488** 0.117* 0.242** 0.209** 1 TSS 0.076 0.022 -0.240** 0.448** 0.120* 0.030 0.158** 1 COD 0.168** 0.146* -0.451** 0.095 0.109 0.020 0.642** 0.074 1 BOD 5 0.159** 0.162** -0.444** 0.096 0.128* 0.017 0.649** 0.091 0.994** 1 Coliform -0.030 0.173** -0.208** 0.226** 0.013 -0.121* 0.179** 0.237** 0.297** 0.302** 1 PO 4 3− 0.286** 0.073 -0.309** 0.085 0.297** 0.162** 0.678** 0.146* 0.510** 0.512** 0.129* 1 Table 3 Eigenvectors and coefficients in the linear combinations of variables making up first two principal components Parameters Spatial groups PC1 PC2 Temp. -0.279 0.165 pH -0.201 -0.213 DO 0.264 -0.137 Turb. 0.105 0.216 NO 3 − -0.135 -0.573 NO 2 − -0.108 -0.590 NH 4 + -0.403 0.034 TSS -0.183 0.371 COD -0.402 0.126 BOD 5 -0.402 0.125 Coliform -0.331 -0.050 PO 4 3− -0.377 -0.111 Eigenvalues 5.34 2.49 Proportion explained (%) 44.5 20.7 Cumulative proportion (%) 44.5 65.3 The first two PCs explained 65.3% of the total variance, suggesting that the fluctuations in SWQ in the study area were complex and influenced by multiple pollution sources. Water quality was higher in the main streams of the Sai Gon and Dong Nai Rivers compared to their tributaries. This discrepancy is attributed to the tributaries being located within the study area, where they are expected to receive more municipal wastewater discharged from residential areas compared to the mainstream zones. The PCA analysis indicated that NO 3 − , NO 2 − , NH 4 + , PO 4 3− , COD, BOD 5 , and coliform could be major pollution sources in the Sai Gon and Dong Nai Rivers, passing through BDP. These parameters could be connected to the residential areas and industrial activities in Thu Dau Mot, Thuan An, and Di An Cities. PC1, having a high loading value with NH 4 + , PO 4 3− , COD, BOD 5 , and coliform parameters, may imply that the primary origin of pollution for these parameters could be linked to improper disposal of urban and industrial sewage. This pollution includes organic matter, nutrients, and microorganisms resulting from the decomposition of organic compounds from domestic and industrial activities, as well as contamination from human and animal feces in water sources. This is demonstrated by the fact that group SGDN1 comprised sites within industrial parks like Song Than I, Song Than II, Thai Hoa, and An Phu, as well as in the urban areas of the two largest cities, Thu Dau Mot and Di An. Particularly, site RSG10 experienced significant wastewater impact from Dong An 1 Industrial Park and domestic sources in the nearby residential areas. In water bodies, organic materials (indicated by BOD 5 and COD) and nutrients (NH 4 + , PO 4 3− ) typically result from the decomposition of dead organisms, along with inputs from domestic and industrial wastewater. The rise in BOD concentration in the midstream section of the Code River was due to increased domestic waste inputs from settlements or communal sewage treatment plants. Additionally, many residents living along the Code River use it as a disposal site for domestic wastewater. These domestic practices likely contribute to organic and nutrient contamination, commonly observed in domestic wastewater (Pratama et al., 2020 ). PC2 primarily accounted for nitrates and nitrites, which were associated with sewage and industrial effluents. This is evidenced by the influence of station RSG8, which affects NO 3 − and NO 2 − , originating from both Rach Bap Industrial Park and nearby residential areas. In brief, the surface water system in the study area is threatened by three primary sources of pollution: organic matter, nutrients, and coliforms. BDP, with its dense population and numerous industrial parks, is likely experiencing water pollution predominantly due to industrial and domestic activities, accounting for about 65.3% of the total variance in the WQI. However, accurately quantifying the impact of domestic activities compared to industrial emissions remains challenging. Currently, the management strategy for water quality in BDP prioritizes industrial zones. It is evident that untreated industrial and domestic wastewater introduces NO 2 − , NO 3 − , PO 4 3− , and NH 4 + into surface water (Lorenz et al., 2021 ). Therefore, addressing nutrient pollution should focus on improving effluent treatment from urban and residential areas, as well as manufacturing facilities. Additionally, agricultural activities may also contribute to the poor SWQ. Thus, while industrial point source emissions may be effectively managed, efforts should be made to enhance nutrient management practices to minimize non‒point sources such as agriculture (Lorenz et al., 2017 ). Conclusion The findings revealed that the surface water quality of the Sai Gon and Dong Nai Rivers, the primary water sources in Binh Duong Province, was compromised by the presence of organic matter, nutrients, and microorganisms. Overall, the water quality was deemed adequate only for navigation and residential purposes following appropriate treatment. Principal Component Analysis (PCA) demonstrated that industrial and urban activities accounted for 65.3% of the variability in water quality. Notably, parameters such as NO 3 − , NO 2 − , NH 4 + , PO 4 3− , COD, BOD 5 , and coliform emerged as significant factors influencing water quality in the study area. Hierarchical Cluster Analysis (CA) categorized the 25 sampling sites into three main groups (DN, SGDN1, SGDN2) and three outlier groups (RSG8, RSG10, and RDN7), with SGDN1, RSG10, and RSG8 requiring more attention due to their inferior water quality. Local environmental managers are encouraged to thoroughly review these findings when planning future water quality monitoring initiatives. Declarations Funding This work was supported by Thu Dau Mot University. Thai Thanh Tran was funded by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2023.TS.107. Conflict of interest The authors of this work declare that they have no conflicts of interest. Author contribution Conceptualization: Thai Thanh Tran. Data curation: Thai Thanh Tran. Formal analysis: Thai Thanh Tran, Hoai Ngoc Pham. Writing original draft: Thai Thanh Tran, Hoai Ngoc Pham, Tuong Dinh Nguyen, Thanh Huyen Thi Phan, My Yen Thi Nguyen, Hoang Yen Thi Tran, Trang Thi Le, and An Ngoc Nguyen. Supervision: Luu Thanh Pham, Quang Xuan Ngo, Quoc Bao Pham. All authors reviewed the manuscript. Data availability The authors confirm that the data supporting the findings of this study are available within the article. 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Application of water quality index for assessment of surface water quality status in Goa. Current World Environment, 9(3), 994-1000. https://doi.org/10.12944/CWE.9.3.54 Thong, N., Nguyen, T. D. T., Nguyen, P. T. T., Le, H. A., Dao, N. K. (2022). Assessment of water quality changes in Saigon and Dong Nai rivers under the influence of domestic wastewater discharges. Science & Technology Development Journal: Science of the Earth & Environment, 6, 468–483. https://doi.org/https://doi.org/10.32508/stdjsee.v6i1.655 Vietnam Environment Administration (VEA). (2019). Decision 1460/QD–TCMT on the Issuing of Technical Guide to Calculation and Disclosure Vietnam Water Quality Index (WQI), Hanoi: Vietnam Environment Administration [in Vietnamese]. Wada, Y., Flörke, M., Hanasaki, N., Eisner, S., Fischer, G., Tramberend, S., Satoh, Y., van Vliet, M.T.H., Yillia, P., Ringler, C., Burek, P., & Wiberg, D. (2016). Modeling global water use for the 21st century: The Water Futures and Solutions (WFaS) initiative and its approaches. Geoscientific Model Development, 9(1), 175-222. https://doi.org/10.5194/gmd-9-175-2016 Zhang, X., Wu, Y., & Gu, B. (2015). Urban rivers as hotspots of regional nitrogen pollution. Environmental Pollution, 205, 139-144. https://doi.org/10.1016/j.envpol.2015.05.031 Zhao, R., Bu, H., Song, X., & Zhang, Y. (2021). A multivariate analysis of the spatial variations of water quality during high-flow period in the Chaobai River (Beijing, China) restored by reclaimed water. Water Supply, 21(6), 3168-3179. https://doi.org/10.2166/ws.2021.088 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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01:18:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4457483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4457483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58754013,"identity":"d149f1c7-5db0-4f52-8f17-2f6a0e19acb1","added_by":"auto","created_at":"2024-06-20 16:21:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":243589,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the sampling locations in the Sai Gon and Dong Nai Rivers, Binh Duong Province\u003c/p\u003e","description":"","filename":"Figure1re.png","url":"https://assets-eu.researchsquare.com/files/rs-4457483/v1/b7ccb736fadc7bb5481f404e.png"},{"id":58754014,"identity":"da57f8ec-a516-42e3-8fd5-53cee02ee683","added_by":"auto","created_at":"2024-06-20 16:21:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1460149,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical dendrogram with SIMPROF test demonstrating the grouping of the sampling sites\u003c/p\u003e","description":"","filename":"Figure2re.png","url":"https://assets-eu.researchsquare.com/files/rs-4457483/v1/b0ed91a6b629cc59150b5b0e.png"},{"id":58754015,"identity":"f12eeedc-cde2-46d8-b9b0-b04fa2331dd5","added_by":"auto","created_at":"2024-06-20 16:21:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1785777,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots and Kruskal‒Wallis (KW) tests illustrate spatial variations in both physiochemical and biological water parameters. Post‒hoc analysis is denoted by the letters a, b, c, and d. Means sharing the same letters is not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026gt; 0.05)\u003c/p\u003e","description":"","filename":"Figure3re.png","url":"https://assets-eu.researchsquare.com/files/rs-4457483/v1/dd4c5f9aa218caca4e9d937b.png"},{"id":58754016,"identity":"074eebcc-881f-4b57-96a8-51de40387f7f","added_by":"auto","created_at":"2024-06-20 16:21:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":251592,"visible":true,"origin":"","legend":"\u003cp\u003eBiplots of PCA results showing key parameters influencing surface water quality\u003c/p\u003e","description":"","filename":"Figure4re.png","url":"https://assets-eu.researchsquare.com/files/rs-4457483/v1/7462ca5d3fde23c3af25f039.png"},{"id":59272786,"identity":"53079b2e-fa04-4bbc-9451-b2a669aed531","added_by":"auto","created_at":"2024-06-28 13:08:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5762647,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4457483/v1/911c407e-be0a-4d4c-a1bb-e110fc19695c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial analysis of surface water quality using multivariate statistical techniques and water quality index: Case study of Binh Duong Province, the largest industrial hub in Southern Vietnam","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater is a crucial natural resource essential for various biological and non‒biological activities (Pandey et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although abundant on Earth, the majority is saline or brackish and unsuitable for consumption or agriculture (Kamboj \u0026amp; Kamboj, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Only around 3% of water is freshwater, with just 1% available for drinking and fulfilling basic needs (Singh \u0026amp; Kamal, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, human activities have caused negative impacts on water sources, notably through the contamination and reduction of surface water (Harding et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Water quality deterioration persists in developed countries, while it is a major problem in developing countries in which a substantial amount of sewage is discharged directly into rivers (John et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mishra et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). There's a rising global apprehension regarding water availability and its quality, with projections indicating a 20 to 30% surge in water demand by 2050 (Wada et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, variations in the hydrological cycle across different regions and times, coupled with uncertainties associated with climate change, could exacerbate this situation (Fan \u0026amp; Shibata, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Putro et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The decline in the SWQ poses a significant threat to human health and elevates the likelihood of waterborne illnesses. Indeed, the utilization of low‒quality water is a contributing factor to the high mortality rate (8.5%) in Southeast Asia (Dairo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn that regard, assessing the quality of surface water has become a critical and essential task in every country. In Vietnam, the government has made considerable efforts to implement a surface water monitoring program through the development of legislative documents like the Environmental Protection Law and the Water Resources Law (Lorenz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Various parameters are measured in the surface water monitoring program to assess water quality. In‒situ measurements include temperature, pH, turbidity, conductivity, total dissolved solids (TDS), salinity, and dissolved oxygen (DO). Laboratory analysis covers biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonia (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), orthophosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e), total petroleum hydrocarbons (TPH), chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e), fluoride (F\u003csup\u003e\u0026minus;\u003c/sup\u003e), heavy metals (Hg, As, Cu, Zn, Cr, Ni, Pb, Cd, Fe), organic chlorine pesticides (aldrin, dieldrin, DDTs, heptachlor \u0026amp; heptachlor epoxide, total organic carbon), cyanide (CN\u003csup\u003e\u0026minus;\u003c/sup\u003e), phenol, and biological indicators (\u003cem\u003eEscherichia coli\u003c/em\u003e, coliform). Determining monitoring parameters and sites for assessing SWQ depends on budgetary constraints and the nature of pollution sources. According to the Vietnamese Ministry of Natural Resources and Environment guidance, SWQ is assessed by comparing parameter values against the national standard (QCVN 08‒MT:2015/BTNMT) (MONRE, 2015).\u003c/p\u003e \u003cp\u003eMoreover, the water quality index (WQI) is computed to categorize surface water quality (VEA, 2019). However, these conventional methods fail to fully exploit all essential information from the monitoring datasets, rendering their results less objective (Nguyen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, in the calculation of WQI, assigning weights to parameters or variables to reflect their significance is a critical step that impacts the comprehensive assessment of water quality (Lkr et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nonetheless, weight-based indices can suffer from imbalanced sensitivity if certain parameters are strongly or weakly weighted. Additionally, these weights are typically determined based on expert judgments, and any alterations in weight assignments can significantly alter the overall interpretation of water quality. For instance, higher assigned weights may disproportionately influence parameters with lower concentrations, leading to inaccurate interpretations of water quality (Juwana et al., 2022). On the other hand, monitoring studies often yield vast and intricate datasets, making it challenging to interpret water quality due to underlying interrelationships among measured parameters (Marinović Ruždjak \u0026amp; Ruždjak, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Univariate statistical analysis (i.e., WQI) struggles to capture this complexity (Le et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, insights from univariate methods typically focus on specific pollution types and may not effectively support integrated water quality management. While composite indices like WQI consider multiple parameters, they primarily communicate water body health rather than reveal dataset structure (Alberto et al., 2011). Consequently, crucial information, such as pollution sources, may remain hidden within WQI scores after normalization and weighting (Le et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, monitoring programs produce extensive datasets, necessitating effective interpretation methods. Multivariate statistics, particularly techniques like Hierarchical Cluster Analysis (CA) and Principal Components Analysis (PCA), are commonly employed to understand relationships between variables and their significance to the studied issue. CA assists in grouping similar variables into clusters, facilitating analysis. On the other hand, PCA serves as a dimension‒reduction tool, condensing large sets of variables into smaller ones while retaining the essential information (Kılı\u0026ccedil; \u0026amp; Y\u0026uuml;cel, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultivariate statistical techniques are extensively utilized in environmental monitoring datasets due to their ability to: (i) accurately reflect the multivariate nature of the system, (ii) handle large datasets effectively, and (iii) detect and quantify multivariate patterns arising from the correlation structure of variables (Kılı\u0026ccedil; \u0026amp; Y\u0026uuml;cel, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Multivariate statistical analysis has been widely accepted and proven effective for various studies, including identifying water quality status in ecological systems, assessing spatial and temporal variations in surface waters, and pinpointing underlying factors contributing to water pollution (Khaledian et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003elı\u0026ccedil; \u0026amp; Y\u0026uuml;cel, 2019; Pratama et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; de Andrade Costa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the context of Vietnam, several studies have highlighted its utility (Giao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pham et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSurface water pollution is a significant environmental issue in Vietnam, especially in the Southeast region, which is crucial for industrial and agricultural growth (Lorenz et al., 2024). Binh Duong Province (BDP), located in the Southeast of Vietnam, is renowned for its bustling industrial landscape. With its strategic position adjacent to Ho Chi Minh City, BDP has become a hub for industrial development, drawing in a multitude of factories and manufacturing plants (BDIZ, 2024). While BDP stands as a symbol of industrial prowess and economic dynamism in Vietnam, it also faces the pressing issue of water pollution due to the concentration of industrial activities (Nguyen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thong et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, surface water resources in BDP are also at risk from pollutants from outside sources, such as urban areas, residential areas, and areas where trade, service, and production are concentrated in the Southeast region. The main rivers and canals in BDP are directly affected by waste discharges from socio-economic development activities, which leads to the need to assess water quality and propose solutions for protection and preservation.\u003c/p\u003e \u003cp\u003eThe study aimed to (i) analyze spatial changes in water quality characteristics and update water quality diagnosis using multivariate analysis and WQI and (ii) identify key contributors to pollution and sources affecting water quality in the BDP. Findings will offer valuable insights into water quality assessment through multivariate statistical methods and assist local governments in formulating effective public policies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eBinh Duong Province has an area of 2694.43 km\u003csup\u003e2\u003c/sup\u003e, with a population of 2,426,561 (2019). The region is subject to a tropical monsoon climate, with a distinct rainy season from May to November and a dry season from December to April. The annual average precipitation ranges from 1.800 mm to 2.000 mm, of which 80% falls in the rainy season. Situated at the heart of the Vietnamese southern key economic region and bordering Ho Chi Minh City, BDP is Vietnam's foremost manufacturing hub with 38 industrial zones (BDIZ, 2024), leading the nation in attracting industrial FDI and poised to evolve into an innovation-driven region.\u003c/p\u003e \u003cp\u003eBinh Duong benefits from its strategic location between the Sai Gon and Dong Nai Rivers, which form an extensive waterways network facilitating domestic and industrial activities. The Dong Nai River, originating from the Lam Vien Plateau, is the largest river in the Southeast region, stretching 635 km in length but only traversing through Tan Uyen District. It serves crucial roles in agriculture, water transport, and fisheries. Meanwhile, the Sai Gon River, spanning 256 km, begins its journey from the mountains of Loc Ninh district in Binh Phuoc province, boasting numerous tributaries, canals, and streams. It flows westward through BDP, with the section from Lai Thieu to Dau Tieng covering a distance of 143 km, characterized by gentle slopes ideal for transportation, agriculture, and fisheries. As it progresses upstream, the river meanders and narrows to 20m, gradually widening to 200m from Dau Tieng to Thu Dau Mot City. However, in the context of economic development, the SWQ in the Sai Gon‒Dong Nai river system has been severely contaminated by a large number of industrial and domestic wastewater pollution sources (Nguyen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thong et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWater sampling and analysis\u003c/p\u003e \u003cp\u003eData were collected at 25 monitoring stations representing the SWQ of the Sai Gon and Dong Nai Rivers in 2021, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Three stations, SG1‒SG3, are situated along the mainstream of the Sai Gon River, while four locations, DN1‒DN4, are positioned along the mainstream of the Dong Nai River. Ten stations designated as RSG1 to RSG10 are situated on tributaries of the Saigon River, while eight stations denoted as RDN1 to RDN8 are located on the Dong Nai River tributaries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMonthly water sampling was conducted at a depth ranging from 0.3 to 0.5 meters below the water surface, with preference given to the central point of the canal, adjusted as per the river or canal's width. Subsequently, these samples were carefully stored in 1‒liter plastic bottles at 4\u0026deg;C and transported to the laboratory of the Natural Resources and Environmental Monitoring Department of BDP for thorough analysis. The sampling, preservation, and processing procedures followed the guidelines established by the Vietnam Environment Administration, referencing specific technical standards: TCVN 6663‒3:2016 (ISO 5667‒3:2012) for water sample preservation and handling, and TCVN 6663‒6:2018 (ISO 5667‒6:2014) for guidance on river and stream sampling techniques.\u003c/p\u003e \u003cp\u003eVarious water quality parameters were measured both \u003cem\u003ein situ\u003c/em\u003e and at the laboratory. In the field, temperature (\u0026deg;C), pH, turbidity (NTU), and dissolved oxygen (DO, mg/L) were measured using handheld meters (pH HQ 11D‒Hach, America; HANNA HI93703‒Hanna; HI9146‒Hanna, Romania), calibrated before each sampling session. Additionally, parameters including biological oxygen demand (BOD\u003csub\u003e5\u003c/sub\u003e, mg/L), chemical oxygen demand (COD, mg/L), total suspended solids (TSS, mg/L), ammonia (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, mg/L), nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, mg/L), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, mg/L), orthophosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, mg/L), and coliform (MPN/100 mL) were analyzed at the laboratory following standard methods (APHA, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The summarized monitoring data is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of water quality in the Sai Gon and Dong Nai Rivers, passing through Binh Duong Province\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurb.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eColiform\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNTU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMPN/100mL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2288.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e900.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e29.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e827.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e240.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e220.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e101.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStnd. skewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-5.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStnd. kurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e223.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e114.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e75.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e53.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro‒Wilk test checked for normal data distribution (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while Levene\u0026rsquo;s test assessed variance homogeneity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Assuming both criteria were met, STATISTICA 7.0 performed one‒way ANOVA. For significant results, Tukey HSD post hoc analysis compared group pairs. Alternatively, the Kruskal‒Wallis test and Bonferroni ranks for all groups were replaced if assumptions weren't met despite data transformation. Spearman rank correlation coefficients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) explored water quality variable relationships.\u003c/p\u003e \u003cp\u003eCluster Analysis (CA) and Principal Component Analysis (PCA) were utilized to examine the spatial patterns of the SWQ across monitoring sites. To ensure accurate analysis and account for measurement unit discrepancies, raw data underwent z‒score transformation, standardizing mean and variance to 0 and 1, respectively (Singh \u0026amp; Kamal, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCA identified similarities in water quality composition among monitoring sites, showcasing high homogeneity within groups and heterogeneity between them (Razmkhah et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Hierarchical clustering, utilizing Euclidean distance, generated dendrograms representing site similarities (Zhao et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The SIMPROF technique verified significantly distinct groups.\u003c/p\u003e \u003cp\u003ePCA elucidated key factors impacting water quality and potential pollution sources (Kowalkowski et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chounlamany et al., 2019). Operating on a correlation matrix, PCA reduced data dimensionality, extracting the contributions of each principal component (PC). Eigenvalues measured the extent to which PCs explained original data, with eigenvectors selected based on the Kaiser‒Guttman criterion, considering only those surpassing the mean eigenvalue (Borcard et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll calculations and graphics were performed in PRIMER v.6 (Anderson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVietnamese water quality index (VN_WQI)\u003c/p\u003e \u003cp\u003eIn order to evaluate the comprehensive quality of water, the Water Quality Index (VN_WQI) is computed following guidelines outlined by the Vietnam Environment Administration (VEA, 2019). The formula for calculating VN_WQI is shown as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVN_WQI =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{WQI}_{pH}}{100}{\\left[\\frac{1}{7}\\sum _{a=1}^{7}{WQI}_{a}\\times \\frac{1}{2}\\sum _{b=1}^{2}{WQI}_{b}\\times {WQI}_{c}\\right]}^{\\frac{1}{3}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003ewhere WQI\u003csub\u003ea\u003c/sub\u003e for DO, BOD\u003csub\u003e5\u003c/sub\u003e, COD, N‒NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, N‒NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, N‒NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, P‒PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e; WQI\u003csub\u003eb\u003c/sub\u003e for TSS and turbidity; WQI\u003csub\u003ec\u003c/sub\u003e for coliform; WQI\u003csub\u003epH\u003c/sub\u003e for pH.\u003c/p\u003e \u003cp\u003eThe VN_WQI ranges from 0 to 100, categorizing water quality into five levels: \u0026lt;10 (heavily polluted, requiring immediate treatment), 10\u0026ndash;25 (poor, requiring urgent treatment), 26\u0026ndash;50 (bad, for transport and equivalent purposes), 51\u0026ndash;75 (moderate, for irrigation and other similar purposes), 76\u0026ndash;90 (good, suitable for domestic water supply with necessary treatment), and 91\u0026ndash;100 (very good, suitable for direct water supply purposes).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussions","content":"\u003cp\u003eCluster analysis of spatial changes in water quality\u003c/p\u003e \u003cp\u003eThe results of cluster analysis with the SIMPROF test showed that the surface water quality at 25 monitoring sites formed three groups and three outlying groups (RSG8, RSG10, and RDN7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first group, termed the DN group, comprised stations DN1, DN2, DN3, and DN4, all situated within the Dong Nai River. The second group, designated as the SGDN1 group, encompassed stations located in the tributaries of the Sai Gon and Dong Nai Rivers, specifically including RSG4, RSG7, RSG9, RDN2, and RDN5. The remaining 13 stations formed the third group, denoted as the SGDN2 group.\u003c/p\u003e \u003cp\u003eThe DN and RDN7 groups displayed the highest mean values of DO and WQI, which were significantly different from the other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the DN group, the mean DO values were 5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 mg/L, while in the RDN7 group, they were slightly lower at 5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 mg/L. The WQI values were 81.41\u0026thinsp;\u0026plusmn;\u0026thinsp;12.92 and 76.94\u0026thinsp;\u0026plusmn;\u0026thinsp;12.24 for the DN and RDN7 groups, respectively. Nutrient indicators such as NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, as well as biochemical indicators like COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform, were found to be at their lowest levels in the DN and RDN7 groups compared to the other groups. Specifically, in the DN group, the mean values of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform were 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 mg/L, 0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 mg/L, 0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 mg/L, 0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 mg/L, 7.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02 mg/L, 3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 mg/L, and 2167.9\u0026thinsp;\u0026plusmn;\u0026thinsp;781.23 MPN/100mL, respectively. According to the WQI, the surface water quality in both the DN and RDN7 groups was classified as good.\u003c/p\u003e \u003cp\u003eConversely, the RSG8, RSG10, and SGDN1 groups exhibited the lowest levels of DO (ranging from 1.84 to 3.83 mg/L) and WQI (ranging from 54.99 to 65.70) in comparison to the other groups. Based on the WQI, the surface water quality in RSG8 and SGDN1 was assessed as moderate, whereas notably, the RSG10 group exhibited bad water quality. Moreover, the nutrient and biochemical indicators in RSG8 and SGDN1 were significantly elevated. Specifically, the mean values of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, TSS, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform in RSG10 were notably high, with corresponding values of 1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 mg/L, 0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/L, 26.34\u0026thinsp;\u0026plusmn;\u0026thinsp;19.11 mg/L, 0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 mg/L, 50.92\u0026thinsp;\u0026plusmn;\u0026thinsp;58.77 mg/L, 96.50\u0026thinsp;\u0026plusmn;\u0026thinsp;61.33 mg/L, 44.67\u0026thinsp;\u0026plusmn;\u0026thinsp;29.19 mg/L, and 2733.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1209.59 MPN/100mL, respectively. Therefore, in the case of RSG10, the DO value was 3.73 times lower than the Vietnamese standard A2 (water for residential use with appropriate treatment, preservation of aquatic plants, or other purposes) (MONRE, 2015), while NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, TSS, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e levels were 87.8, 1.69, 6.43, 7.44, and 4.1 times higher than the A2. (MONRE, 2015). Additionally, RSG8 recorded elevated values of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, at 6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82 mg/L and 0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65 mg/L, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the SGDN2 group demonstrated relatively low mean values of DO, measuring at 2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32 mg/L, while the WQI indicated an average level (74.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64). Additionally, the mean values of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform were relatively high, corresponding to 1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75 mg/L, 0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 mg/L, 2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06 mg/L, 0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 mg/L, 15.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.51 mg/L, 7.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48 mg/L, and 2240.61\u0026thinsp;\u0026plusmn;\u0026thinsp;851.49 MPN/100mL, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The WQI categorized the surface water quality in the SGDN2 group as moderate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis and key variables influencing water quality\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates significant correlations among various water quality parameters (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Temperature, pH, DO, and turbidity correlated with most other parameters. DO exhibited a strong negative correlation with nutrient parameters (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e) and biological indicators (COD, BOD\u003csub\u003e5\u003c/sub\u003e, coliform), likely indicating the impact of DO on the dynamics of reactive nitrogen in surface water (Le et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Elevated levels of nutrients in water could potentially result in decreased DO levels owing to the biological oxidation-reduction processes involving algae and bacteria (Hong et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; de Almeida Fernandes et al., 2020). Additionally, an uptick in organic matter (BOD\u003csub\u003e5\u003c/sub\u003e) is associated with heightened nutrient concentrations (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) (Giao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, this increase might lead to elevated respiration and organic matter decomposition, ultimately diminishing the DO concentration and detrimentally impacting water quality.\u003c/p\u003e \u003cp\u003ePCA extracted the two most representative PCs accounting for 65.3% of the total variance in the data set of the SWQ. PC1, which accounted for 44.5% of the total variance, was positively correlated with DO and turbidity but negatively correlated with most other parameters. Moreover, PC1 was strong negatively correlated with NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, coliform, and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e. Therefore, PC1 mainly represented organic and biological pollution. PC2, with 20.7% of the total variance, had positive correlations with temperature, turbidity, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, TSS, COD, and BOD\u003csub\u003e5\u003c/sub\u003e, strong and negative correlations with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e. Thus, PC2 predominantly represented water pollution of nitrates and nitrites (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe surface water quality of groups RDN7, DN (DN1, DN2, DN3, DN4), SGDN2 (SG1, SG2, SG3, RSG1, RSG2, RSG3, RSG5, RSG6, RDN1, RDN3, RDN4, RDN6, RDN8) exhibited high DO) levels and low concentrations of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, coliform, and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, indicating good surface water quality. Group RSG8 displayed relatively high DO levels and low concentrations of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, COD, and BOD\u003csub\u003e5\u003c/sub\u003e; however, due to elevated NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e levels, it was categorized as having moderate surface water quality. In contrast, groups RSG10, SGDN1 (RSG4, RSG7, RSG9, RDN2, RDN5) were characterized by low DO levels and high concentrations of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, coliform, and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, leading to classification as having bad and moderate surface water quality, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman rank correlation of 12 water quality variables in Sai Gon and Dong Nai Rivers, Binh Duong Province\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurb.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eColiform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd 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colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.160**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.197**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.118*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.116*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.491**\u003c/p\u003e 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colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.240**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.448**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.120*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.158**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.168**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.146*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.451**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.642**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.159**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.162**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.444**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.128*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.649**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.994**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColiform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.208**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.226**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.121*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.179**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.237**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.297**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.302**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.286**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.309**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.297**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.162**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.678**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.146*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.510**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.512**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.129*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvectors and coefficients in the linear combinations of variables making up first two principal components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSpatial groups\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColiform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion explained (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative proportion (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first two PCs explained 65.3% of the total variance, suggesting that the fluctuations in SWQ in the study area were complex and influenced by multiple pollution sources. Water quality was higher in the main streams of the Sai Gon and Dong Nai Rivers compared to their tributaries. This discrepancy is attributed to the tributaries being located within the study area, where they are expected to receive more municipal wastewater discharged from residential areas compared to the mainstream zones.\u003c/p\u003e \u003cp\u003eThe PCA analysis indicated that NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform could be major pollution sources in the Sai Gon and Dong Nai Rivers, passing through BDP. These parameters could be connected to the residential areas and industrial activities in Thu Dau Mot, Thuan An, and Di An Cities. PC1, having a high loading value with NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform parameters, may imply that the primary origin of pollution for these parameters could be linked to improper disposal of urban and industrial sewage. This pollution includes organic matter, nutrients, and microorganisms resulting from the decomposition of organic compounds from domestic and industrial activities, as well as contamination from human and animal feces in water sources. This is demonstrated by the fact that group SGDN1 comprised sites within industrial parks like Song Than I, Song Than II, Thai Hoa, and An Phu, as well as in the urban areas of the two largest cities, Thu Dau Mot and Di An. Particularly, site RSG10 experienced significant wastewater impact from Dong An 1 Industrial Park and domestic sources in the nearby residential areas. In water bodies, organic materials (indicated by BOD\u003csub\u003e5\u003c/sub\u003e and COD) and nutrients (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e) typically result from the decomposition of dead organisms, along with inputs from domestic and industrial wastewater. The rise in BOD concentration in the midstream section of the Code River was due to increased domestic waste inputs from settlements or communal sewage treatment plants. Additionally, many residents living along the Code River use it as a disposal site for domestic wastewater. These domestic practices likely contribute to organic and nutrient contamination, commonly observed in domestic wastewater (Pratama et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). PC2 primarily accounted for nitrates and nitrites, which were associated with sewage and industrial effluents. This is evidenced by the influence of station RSG8, which affects NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, originating from both Rach Bap Industrial Park and nearby residential areas.\u003c/p\u003e \u003cp\u003eIn brief, the surface water system in the study area is threatened by three primary sources of pollution: organic matter, nutrients, and coliforms. BDP, with its dense population and numerous industrial parks, is likely experiencing water pollution predominantly due to industrial and domestic activities, accounting for about 65.3% of the total variance in the WQI. However, accurately quantifying the impact of domestic activities compared to industrial emissions remains challenging. Currently, the management strategy for water quality in BDP prioritizes industrial zones. It is evident that untreated industrial and domestic wastewater introduces NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e into surface water (Lorenz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, addressing nutrient pollution should focus on improving effluent treatment from urban and residential areas, as well as manufacturing facilities. Additionally, agricultural activities may also contribute to the poor SWQ. Thus, while industrial point source emissions may be effectively managed, efforts should be made to enhance nutrient management practices to minimize non‒point sources such as agriculture (Lorenz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings revealed that the surface water quality of the Sai Gon and Dong Nai Rivers, the primary water sources in Binh Duong Province, was compromised by the presence of organic matter, nutrients, and microorganisms. Overall, the water quality was deemed adequate only for navigation and residential purposes following appropriate treatment. Principal Component Analysis (PCA) demonstrated that industrial and urban activities accounted for 65.3% of the variability in water quality. Notably, parameters such as NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform emerged as significant factors influencing water quality in the study area. Hierarchical Cluster Analysis (CA) categorized the 25 sampling sites into three main groups (DN, SGDN1, SGDN2) and three outlier groups (RSG8, RSG10, and RDN7), with SGDN1, RSG10, and RSG8 requiring more attention due to their inferior water quality. Local environmental managers are encouraged to thoroughly review these findings when planning future water quality monitoring initiatives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Thu Dau Mot University. Thai Thanh Tran was funded by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2023.TS.107.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this work declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Thai Thanh Tran. Data curation: Thai Thanh Tran. Formal analysis: Thai Thanh Tran, Hoai Ngoc Pham. Writing original draft: Thai Thanh Tran, Hoai Ngoc Pham, Tuong Dinh Nguyen, Thanh Huyen Thi Phan, My Yen Thi Nguyen, Hoang Yen Thi Tran, Trang Thi Le, and An Ngoc Nguyen. Supervision: Luu Thanh Pham, Quang Xuan Ngo, Quoc Bao Pham. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlberto, W. D., del Pilar, D. M., Valeria, A. M., Fabiana, P. S., Cecilia, H. A., \u0026amp; de Los \u0026Aacute;ngeles, B. M. (2001). 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Water Supply, 21(6), 3168-3179. https://doi.org/10.2166/ws.2021.088\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cluster analysis, industrial pollution, principal component analysis, water quality management, water resources","lastPublishedDoi":"10.21203/rs.3.rs-4457483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4457483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnsuring high‒quality water supply is essential for both domestic and manufacturing activities, particularly in Binh Duong Province (BDP), situated at the heart of Vietnam's southern key economic region, known for its dense population and numerous industrial parks. In this study, multivariate statistical analysis techniques, including Hierarchical Cluster Analysis (CA) and Principal Component Analysis (PCA), were employed to assess spatial variations in surface water quality (SWQ) along the Sai Gon and Dong Nai Rivers, which are the two primary water bodies in BDP. CA classified the 25 sampling sites into three groups (DN, SGDN1, SGDN2) and three outlying groups (RSG8, RSG10, and RDN7). Groups RDN7 and DN were deemed to have good surface water quality, while RSG8 exhibited moderate SWQ. Conversely, RSG10 and SGDN1 were classified as having bad and moderate surface water quality, respectively. The Kruskal‒Wallis test revealed significant spatial differences in all water quality parameters among the six clusters (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PCA identified two principal components (PCs) explaining 65.3% of the total variance, highlighting NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, COD, BOD\u003csub\u003e5\u003c/sub\u003e, and coliform as major pollution sources in the area. The findings underscore the impact of untreated domestic and industrial sewage on water quality in the Sai Gon and Dong Nai Rivers. This study contributes valuable insights into water quality assessment using multivariate statistical methods and informs the formulation of effective public policies by local governments.\u003c/p\u003e","manuscriptTitle":"Spatial analysis of surface water quality using multivariate statistical techniques and water quality index: Case study of Binh Duong Province, the largest industrial hub in Southern Vietnam","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 16:20:49","doi":"10.21203/rs.3.rs-4457483/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cbd1544-22b2-43a4-94b8-185f054c277f","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-28T13:00:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 16:20:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4457483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4457483","identity":"rs-4457483","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}