Correction to: Discover Applied Sciences (2024) 6:310 https://doi.org/10.1007/s42452-024-06013-x


In this article, several reviewers' comments regarding an earlier version were erroneously included in the published text.

The original article has been corrected. The removed comments are the underlined parts in the paragraphs reprinted below.


In the first paragraph of section 1:


Surface water resources act as sinks for pollutants arising from both human activities and natural processes [1, 2], with the former identified as the primary driver of pollution [3, 4]. This is evident when untreated wastewater is directly discharged into receiving surface water bodies, including rivers, canals, lakes, and ponds [5, 6]. As a result, ecological impacts due to organic pollution of fluvial components tends to increase from upstream to downstream [7], with higher levels of organic contaminants and ecological impacts observed in fluvial components during the wet season [8]. (Explain in more detail how this previous sentence relates to the following sentences) Due to the compounding effect of climate change, socioeconomic development, changing land-use practices, industrialisation, and increased water usage by upstream countries, the freshwater sources in the Vietnamese Mekong Delta (VMD) are witnessing a decline in both quantity and quality. Therefore, the development of a comprehensive monitoring and management scheme is imperative to oversee the changes to these vital resources and to contribute to safeguarding existing resources. Monitoring data on surface water quality helps managers understand the current state of water quality, serving as the foundation for identifying the factors contributing to pollution and suggesting mitigating solutions for water resource management [9–11].


In the last paragraph of section 1:


Proper identification of discharge sources impacting surface water quality helped state management in implementing effective management plans and solutions [21, 27]. According to Bostanmaneshrad et al. and Mijares et al. [21, 30], the determination of pollution load is crucial to assess the impact of waste sources on the pollution status of the receiving source. However, in the VMD, identifying the pollutant loads to surface water quality resources still has numerous limitations. The studies frequently assess water quality using monitoring data, prompting subsequent discussions on related studies involving pollutants (What “substances”?); however, there is often a lack of clarity regarding specific waste sources associated with these evaluations. Therefore, this research aims to enhance the understanding of waste source identification impacting water quality in Vinh Long Province by (1) calculating the pollution load from discharge sources, which encompass both point and non-point sources, and (2) employing multivariable statistical analysis to assess precisely the influence of wastewater sources on surface water quality.


In the first three paragraphs of section 2.2:


(what is “secondary” data?) Wastewater from domestic, cage aquaculture, and storm-water runoff are categorized as NPS, while wastewater from livestock, slaughterhouses, craft villages, pond aquaculture, industrial zones, healthcare facilities, food and seafood processing plants are considered as PS. The estimation of pollutant load is based on both the quantity and concentration of waste produced.

Wastewater samples were collected at the discharge of each wastewater treatment system as a PS, while domestic wastewater was collected from 2 locations at sewers in residential and urban areas from June–October 2021, with sampling conducted every ten days for the calculation of the pollution load. (Need a citation for TCVN and ISO and SME etc.—you cannot assume readers know these sources of methodologies) The sample collection follows the standard procedure according to TCVN 5999:1995 of the Ministry of Science and Technology of Vietnam [32] and ISO 5667–10:1992 of the International Organization for Standardization [33] and the preservation procedures according to TCVN 6663–3:2016 of the Ministry of Science and Technology of Vietnam [34], before being analyzed in the laboratory. Wastewater analysis parameters including BOD5, COD, TP, and TN were done using the corresponding methods: SMEWW 5210B:2017, SMEWW 5220C:2017, SMEWW 4500-P.B&E: 2017, and TCVN 6638:2000 according to the American Public Health Association, American Water Works Association, & Water Environment Federation, and the Ministry of Science and Technology of Vietnam [35, 36].

Sixty surface water monitoring data at fixed points (points or stations?) were systematically collected by the Department of Natural Resources and Environment of Vinh Long Province to assess the surface water quality during three distinct periods, including March (representing the annual dry season), June (representing the transitional season), and September (representing the wet season) within the timeframe of 2017–2021. The sample locations were selected to provide a comprehensive representation of surface water conditions across the province. These sampling fixed points were strategically distributed to cover over 30 rivers of different channel widths, ensuring a balanced coverage of the areas influenced by wastewater discharge by both PS and NPS.


In the first two paragraphs of section 3.1:


The physicochemical and microbiological characteristics of surface water in three periods, March (dry season), June (transitional season), and September (wet season) from 2017 to 2021 in Vinh Long are represented in Fig. 2. The water temperature in March was higher than that in June and September. The pH varied between 7.22 ± 0.08 and 7.77 ± 0.07, peaking during the dry season at an average of 7.49 ± 0.14. This value stayed consistent and under the threshold of the QCVN 08:2015 (column A2). The average EC value was 57 ± 22.6 mS/m, ranging from 32.8 ± 6.4 to 101.8 ± 40.9 mS/m. (note that EC can be a good indicator of rainfall and water flow, with EC increasing during low flow periods…… thus, does this agree with water flow or rainfall?) EC in March (101.8 ± 40.9 mS/m) was higher than the numbers in June (37.1 ± 20.7 mS/m) and September (32.8 ± 6.4 mS/m). This shows that as rainfall increases, EC tends to decrease in June and September (Fig. 2n).

The TSS concentration ranged from 28.5 ± 3.87 to 56.1 ± 13.9 mg/L, with an average of 41.2 ± 6.96 mg/L. At most monitoring fixed points, the concentration fell within the acceptable range of EPA criteria but exceeded the QCVN 08:2015 (30 mg/L) at some sites. The monitoring locations exceeding QCVN 08:2015 were 60 in September, 53 in June, and 51 in March. The turbidity ranged from 40.58 ± 14.73 NTU to 84.74 ± 43.42 NTU, with an average of 59.6 ± 8.09 NTU. TSS in September (56.1 ± 13.9 mg/L) was higher than those in June (28.5 ± 3.9 mg/L) and March (38.9 ± 3.1 mg/L). As a result, TSS increases with rainfall. (These parameters also trend with water flow and rainfall in most places. Was this true for these data?).


In the sixth paragraph of section 3.1:


E.coli density ranged from 56.8 ± 8.4 MPN/100 mL to 489 ± 33.6 MPN/100 mL, with an average of 191 ± 109 MPN/100 mL. (Note that when parameters do not show a normal bell curve, then the median or geometric mean is a better representation than the average or mean. Consider this for all data.) Similarly, a high density of coliform was found with a range of 3166 ± 80.83 MPN/100 mL to 42,220 ± 26,806 MPN/100 mL, while its average was 10,517 ± 8771 MPN/100 mL. In March, June, and September, all monitoring station except for NM18 and NM40 exceeded the QCVN 08:2015 standard for E. coli. Similarly, in September, all monitoring fixed points exceeded the standard for total coliform. There were sampling fixed points with E. coli and coliform density over the QCVN 08:2015 standard dispersed in all river systems over Vinh Long Province. E. coli and coliform densities were, respectively, 9.55 and 8.44 times greater than the permissible limits (50 MPN/100 mL for E. coli and 5,000 MPN/100 mL for coliform). (Did E. coli and coliform levels follow rainfall events and continue to stay high for some time afterwards?).


In the eighth and ninth paragraphs of section 3.1:


The EC in the current study area was higher than Tien Giang Province (40.59–99.28 mS/m) [37], which was associated with the influence of geographic location and flow rate on the same Tien River. Vinh Long Province is near an estuary where the EC changes depending on ions in the ocean water. The EC value found in the study area was still in the threshold of 15–50 mS/m, which is suitable for fish and macroinvertebrates in freshwater environments [37]. High EC values in surface water are the most common in residential districts, industrial zones, and central markets where a substantial proportion of wastewater contains organic matters, resulting in greater mineral segregation [31]. TSS and water turbidity during September were higher than in March and June, which could be attributed to rainwater runoff and soil disturbance during agricultural production and cropping seasons. (Turbidity and TSS can indicate that high levels of sediment are introduced which can physically bury macroinvertebrates and effect fish. This could be important.) Turbidity and TSS can indicate that high levels of sediment are introduced which can physically bury macroinvertebrates and effect fish.

The DO concentration obtained was similar to those in the same study area in 2019 [31], (more literature review may be needed to state that DO does not go low for sensitive species of macroinvertebrates and fish Values below 4 at certain water temperatures can be limiting.). Vinh Long Province's DO was greater than other nearby provinces, including Tien Giang (3.2–4.0 mg/L) [54], Hau Giang (3.2–5.2 mg/L) [55], and Dong Thap (4.73–5.55 mg/L) [53]. Vinh Long's river system's greater water surface area makes it simpler for oxygen from the atmosphere to diffuse into the surface water [31]. There was an increase in DO because rain runoff from agriculture and aquaculture, along with the discharge of residential garbage, severely mixed with the river flow in the research locations. However, measurements of BOD5 and COD show organic matter contamination as an important factor along the Hau River sampling fixed points.


In the caption to Table 5:


Table 5 Comparing the parameters of water quality in each cluster to QCVN 08:2015 (are these average values and note that the mean may not be the best statistic for log-normal or non-normal data such as E. coli and coliform)


In the first two paragraphs of section 3.3:


The CA analysis (Fig. 4) findings reveal 3 groups formed by clustering at a distance (Dlink/Dmax) × 100 equaled 95 ??. Group 1 comprised 29 monitoring stations (NM01–NM04, NM06–NM16, NM23–NM26, NM30, NM34, NM35, NM39–MN41, NM47–NM49, NM56). These locations are primarily affected by domestic wastewater from urban areas, residential areas, and aquaculture activities. The wastewater from these sources has led to the parameters TSS, BOD5, N–NH4+, COD, P–PO43− exceeding QCVN 08:2015. Group 2 comprised 3 monitoring sites (NM27, NM28, NM29) mainly impacted by the same waste sources as group 1, along with runoff water carrying pollutants leading to elevated levels of TSS, BOD5, P-PO43, coliform and E. coli, exceeding QCVN 08:2015. Group 3 included 28 monitoring stations (NM05, NM17–NM22, NM31–NM33, NM36–NM38, NM42–NM46, NM50–NM55, NM57–NM60), which were influenced by agricultural activities (fruit trees, and rice cultivation) and domestic wastewater, resulting in exceeding QCVN 08:2015 for TSS, BOD5, N–NH4+, P–PO43−, coliform, and E. coli.

The study area’s complex spatial effects of water quality are highlighted through the division of groups from 60 sites between 2017 and 2021 (Table 5). Group 1 included monitoring stations in large rivers and around residential areas that received various sources of pollutants loaded from both domestic and aquaculture wastewater and were considered the least polluted, showing that all parameters insignificantly exceeded the standard QCVN 08:2015, yet the DO value was relatively high. The average DO concentration in the study area was higher than in neighboring provinces [31]. Meanwhile, Group 2 was mainly affected by rain overflow that led to high values of TSS, BOD5, P-PO43−, coliform, and E. coli, exceeding QCVN 08:2015 standard and causing organic pollution in this area. Group 3 experienced notable impacts from agricultural activities, including fruit and rice cultivation, as well as domestic wastewater. This resulted in elevated levels of TSS, BOD5, N–NH4+, P–PO43−, coliform, and E. coli, surpassing the QCVN 08:2015. In short, the impact of pollutant sources caused a difference in water quality among the three groups. The pollutant sources are determined by the different river/canal levels, the distance between production facilities and rivers, and the land use plan. (Did the smaller sections of the rivers with lower water flow show greater impacts?) The water quality in each group was recorded with the same impact sources with similar parameters exceeding the QCVN 08:2015. This could suggest simplifying the sampling data set and cutting the monitoring periods for monitoring tasks. Similarly, in the VMD, other water bodies have reported successful applications of CA techniques in water quality monitoring design [40].


In section 4, the last two paragraphs in their entirety:


(Another way to express this is that both PS and NPS pollution needs attention. If solutions to both were equally achievable, then NPS should be focused on more heavily than PS at first. However, all sources of pollution need attention and a variety of factors beyond this study must be considered in order to devise management plans for these important watersheds.)

Possible additional research: Consider using the groups of sampling stations/watershed regions and selecting for each a representative sampling station. Then look at the sampling station’s data in each case and see if it makes sense. Also, this could be important for designating where to place sampling stations for longer term water quality monitoring. What I see in Fig. 2 is that when temperature increases due to slower water flows, water flow is lower, EC increases due to less dilution, and DO goes down due to less turnover in the water. When water temperature decreases (due to rainfall and higher water flow) then turbidity and TSS go up (runoff), DO increases (water flow), BOD and COD increase (waste input), NH4 increases (waste and runoff), E. coli and total coliform increase (waste increases and runoff too). How long do these increased pollutants have this effect? How do different sized storms affect the magnitude of the pollution? All of this makes sense in most rivers. Taking a look at the data at the individual station level helps to verify the conclusions raised by the compiled data analyses. This is beyond the scope of this paper but may provide good continued research).