Abstract

Proper water quality monitoring is a valuable tool for water resource management, helping to identify polluting sources and risks related to the use of water resources. One of the main types of contamination found in Brazilian water bodies is fecal contamination, which originates mainly from point source pollution through wastewater disposal. Thus, this study analyzed water quality monitoring data from the responsible environmental body (Minas Gerais Institute of Water Management, IGAM), related to the fecal contamination indicator (FCI), for the years 2000–2018. This was done for the Minas Gerais portion of the São Francisco River basin (SFRB-MG), one of the most important basins in the country. The 10 sub-basins in the area were compared using statistical tools. The work found significant differences between the sub-basins in terms of FCI concentration, highlighting the most impacted ones (SF2, SF3, and SF5) as also being the most densely populated. It is necessary to invest in sanitation measures in order to ensure that water resources are preserved, as well as to reduce the public health risks of downstream municipalities that are supplied with previously contaminated water.

INTRODUCTION

The lack of collection and treatment of wastewater directly affects water quality and has become a major problem in the environmental, social, and public health spheres. It is essential to note the ecological imbalance caused by the disposal of untreated effluents into water bodies, and the risk associated with the supply of water that was previously contaminated by the release of effluents (ANA 2017). The disposal of untreated or insufficiently treated domestic wastewater is the main source of pressure on Brazilian water bodies (ANA 2012).

According to the National System of Information on Sanitation (SNIS 2017), 59.7% of the Brazilian population was served by a wastewater collection service in 2016, meaning that approximately 100 million people utilized alternative measures such as septic tanks or even directly releasing effluents into the environment. A study disclosed by the National Water Agency and the Ministry of Cities of Brazil (ANA 2017) exposes the situation of the Brazilian urban population regarding wastewater services: 43% of the population is supplied by a collective system (collective network and wastewater treatment plant – WWTP), 12% is served by an individual solution (septic tanks), 18% has its wastewater collected but not treated, and 27% is not supplied, meaning there is no treatment or collection of wastewater.

In 2015, 44.69% of the urban population in the state of Minas Gerais (MG) had their wastewater treated through regularized WWTPs (FEAM 2016). This was consistent with the evaluation made by the National Water Agency (ANA 2017), where there was a 44% index of the population served by wastewater treatment in the state. In developing countries, river water subject to wastewater contamination is often used for domestic supply (Rochelle-Newall et al. 2016). Most of the municipalities in Minas Gerais (60%) are supplied exclusively by surface water and 20% are supplied simultaneously by surface and underground water sources (ANA 2010). Poor sanitation contributes to public health risks. In 2016, the rate of hospitalizations for diseases associated with inadequate sanitation in Minas Gerais was 135.3 per 100,000 inhabitants (IBGE 2017). In the São Francisco River basin, 97% of the urban population is served by the water supply network (ANA 2012).

The National Water Agency (ANA 2017) warns that from all generated organic load (9.1 thousand tons of biochemical oxygen demand (BOD)·day−1) only 39% is removed using the current wastewater treatment infrastructure in the Brazilian municipalities. As a result, approximately 5.5 thousand tons of BOD·day−1 can reach receiving water bodies, in terms of residual organic load.

Thus, water bodies continue to flow with a high load of pollutants, representing a risk to the environment and public health, which may affect current and future downstream uses of water resources. A water quality monitoring program evaluating physicochemical and biological parameters is able to identify different pollutants in waterways and quantify polluting agents (Zhang et al. 2011). The Minas Gerais Institute of Water Management (IGAM), through the program Águas de Minas (Minas Waters Project), has conducted water quality monitoring in the state since 1997 (IGAM 2016). Microbiological fecal contamination parameters are analyzed, which determine the concentration of indicators of the presence of pathogenic organisms in the water.

The monitoring generates an extensive database containing many sites and long sampling periods, and it is thus necessary to conduct analyses of data series to guarantee proper management of the water resources in the state (Minas Gerais). Statistical tools are useful for data interpretation and to discover more accessible results for public policies and actions focused on water resource management (Simeonov et al. 2003; Ouyang et al. 2006; Zhang et al. 2011).

The current work thus aims to evaluate surface water quality monitoring data from the São Francisco River basin in Minas Gerais state (SFRB-MG), as a function of fecal contamination indicators considering a large time series (2000–2018). The sub-basins from the region were compared to identify those that were the most impacted, in terms of FCI concentration. Trend tests were carried out to verify if FCI concentration increases or decreases in the study area. This deep and wide water quality analysis of the impact of the fecal contamination in one of the most important basins in Brazil may help basin managers with their decision-making.

METHODS

Study area

The São Francisco River basin is the largest watershed fully located in Brazil, with a drainage area of 638,466 km2, occupying 7.5% of the Brazilian territory (CBHSF & ANA 2004). Due to its great socioeconomic importance to Brazil, connecting many regions and states (503 municipalities from the states of Minas Gerais, Bahia, Pernambuco, Alagoas, Sergipe, Goiás, and the Federal District), the São Francisco River is known as the ‘River of the National Unity’. The hydrographic region is divided into four physiographic regions: Upper São Francisco, Middle São Francisco, Lower-Middle São Francisco, and Lower São Francisco (MMA 2006), as illustrated in Figure 1. Due to its large territory, the basin covers cities with very distinct population densities as well as prevailing economic activities. The basin is characterized by its agricultural activities and hydroelectric potential (CBHSF & ANA 2004). The region has a total population of approximately 14.3 million inhabitants, according to the Brazilian Institute of Geography and Statistics (IBGE 2010), with a population growth of 11.3% between 2000 and 2010 (ANA 2012). In terms of domestic effluent collection and treatment, 62% of the wastewater generated in the basin is collected, and 63% of this is treated (ANA 2015). Thus, only approximately 40% of the total generated wastewater is treated.

Figure 1

Location of the São Francisco River basin and its four physiographic regions (Upper São Francisco, Middle São Francisco, Lower-Middle São Francisco, and Lower São Francisco).

Figure 1

Location of the São Francisco River basin and its four physiographic regions (Upper São Francisco, Middle São Francisco, Lower-Middle São Francisco, and Lower São Francisco).

The gross domestic product (GDP) of the basin was almost 250 billion reals in 2012, which corresponds to 5.7% of Brazil's total generated wealth that year (4,392 billion reals, approximately 8,600 billion dollars). The most important contribution is from the Upper São Francisco region (72%) (Nemus 2015), which is fully located in the state of Minas Gerais.

The São Francisco River spring is located in the municipality of São Roque de Minas/MG, and approximately 37% of the basin is within the state area, which is the object of the current study. In this state, the basin is divided into 10 Water Resource Management Units (WRMUs), which are physico-territorial units that comprise areas of basins, groups of basins or contiguous sub-basins with similar natural, socio-cultural and economic characteristics. The 10 WRMUs, also called sub-basins, are: High São Francisco River (SF1); Pará River (SF2); Paraopeba River (SF3); Três Marias Reservoir (SF4); Velhas River (SF5); Jequitaí and Pacuí Rivers (SF6); Paracatu River (SF7); Urucuia River (SF8); Pandeiros and Calindó Rivers (SF9); and Verde Grande River (SF10) (CERH 2010). Table 1 shows the population density in each WRMU.

Table 1

Population density in each WRMU of SFRB-MG

WRMUPopulation density (residents per km2)
SF1 – High São Francisco River 16.10 
SF2 – Pará River 62.68 
SF3 – Paraopeba River 93.24 
SF4 – Três Marias Reservoir 9.21 
SF5 – Velhas River 164.04 
SF6 – Jequitaí and Pacuí Rivers 10.92 
SF7 – Paracatu River 6.81 
SF8 – Urucuia River 3.58 
SF9 – Pandeiros and Calindó Rivers 8.75 
SF10 – Verde Grande River 27.10 
WRMUPopulation density (residents per km2)
SF1 – High São Francisco River 16.10 
SF2 – Pará River 62.68 
SF3 – Paraopeba River 93.24 
SF4 – Três Marias Reservoir 9.21 
SF5 – Velhas River 164.04 
SF6 – Jequitaí and Pacuí Rivers 10.92 
SF7 – Paracatu River 6.81 
SF8 – Urucuia River 3.58 
SF9 – Pandeiros and Calindó Rivers 8.75 
SF10 – Verde Grande River 27.10 

Source: Adapted from IGAM (2014).

Structuration and systematization of the database

Surface water quality monitoring data were obtained from the IGAM Minas Waters Project. The time period selected for the studied area (SFRB-MG) comprised the first trimester of 2000 until the second trimester of 2018.

It is important to emphasize that, from the first data retrieving of 2013, the parameter Escherichia coli (E. coli) started to be evaluated, replacing thermotolerant coliforms. According to the Joint Normative Deliberation COPAM/CERH n. 01/2008, E. coli is the only species of thermotolerant coliform whose habitat is the intestines of humans and homeothermic animals, where it appears in high densities. Hence, it is rarely found in water or soil that has not received fecal contamination (COPAM & CERH 2008). Thus, the parameters selected for the analyses in the selected time period were E. coli and thermotolerant coliforms, composing the fecal contamination indicator (FCI).

The detection method of FCI used in IGAM Minas Waters Project is the multiple-tube fermentation technique (Costa et al. 2017). Water quality analyses were performed in a laboratory accredited by the National Institute of Metrology, Quality and Technology (INMETRO), and followed Standard Methods for Examination of Water and Wastewater (APHA et al. 2012).

A total of 258 monitoring stations was analyzed in the study area, which had a sampling frequency of every three months (every month in a few cases of some stations located in the Velhas River channel).

Table 2 shows the number of monitoring sites analyzed in each WRMU from the SFRB-MG, while Figure 2 shows the location of the stations, different according to their rating class. In Brazil, inland water bodies are rated into five classes, each with target values for water quality parameters that must be achieved to maintain the quality of these water bodies, compatible with the locally predominant water uses. ‘Special class’ water bodies are intended for the most restrictive uses, such as human consumption after simplified treatment, and preservation of aquatic communities. The natural conditions of ‘Special class’ water bodies must be maintained. Hence, there are no target values specified in the legislation. ‘Class 1’ water bodies are intended for human consumption after simplified treatment and for recreation, while ‘Class 2’ water bodies require conventional treatment for human consumption. ‘Class 3’ water bodies require even more advanced treatment for human consumption. Meanwhile, ‘Class 4’ water bodies are intended for less demanding uses, such as navigation.

Table 2

Monitoring sites analyzed in each WRMU of SFRB-MG

WRMUNumber of water quality monitoring sites
SF1 – High São Francisco River 
SF2 – Pará River 29 
SF3 – Paraopeba River 33 
SF4 – Três Marias Reservoir 18 
SF5 – Velhas River 82 
SF6 – Jequitaí and Pacuí Rivers 
SF7 – Paracatu River 30 
SF8 – Urucuia River 13 
SF9 – Pandeiros and Calindó Rivers 16 
SF10 – Verde Grande River 21 
Total 258 
WRMUNumber of water quality monitoring sites
SF1 – High São Francisco River 
SF2 – Pará River 29 
SF3 – Paraopeba River 33 
SF4 – Três Marias Reservoir 18 
SF5 – Velhas River 82 
SF6 – Jequitaí and Pacuí Rivers 
SF7 – Paracatu River 30 
SF8 – Urucuia River 13 
SF9 – Pandeiros and Calindó Rivers 16 
SF10 – Verde Grande River 21 
Total 258 
Figure 2

Location of the evaluated monitoring sites, highlighted by WRMU and rating class.

Figure 2

Location of the evaluated monitoring sites, highlighted by WRMU and rating class.

In Minas Gerais, the Joint Normative Deliberation COPAM/CERH n. 01/2008 regards the water quality standards for each rating class, as well as the standards for disposal of effluents in recipient water bodies. The legislation establishes that E. coli can be determined in order to replace thermotolerant coliforms, respecting the same limits. The maximum permitted values for fresh water, according to each class, are 200 colony-forming units (CFU)·100 mL−1 for class 1; 1,000 CFU·100 mL−1 for class 2; and 4,000 CFU·100 mL−1 for class 3. There are no target values for class 4.

Comparison between WRMUs

First, the descriptive statistics for the data from each WRMU of the study area were calculated. The Kruskal–Wallis nonparametric test was applied, followed by the multiple comparison test, when applicable, that is, when there is a significant difference between the concentrations of FCI in the sub-basins, at a 5% significance level (Conover 1999; Helsel & Hirsch 2002). In this stage, all data retrieved in the period for each WRMU were considered in all monitoring sites of each sub-basin. For the analyses, the software Statistica 13.0 was utilized.

The violation percentage was calculated for each WRMU, considering each monitoring site rating, according to the legally established limits. For the sites rated as ‘Special class’, the value for Class 1 was adopted, since according to the Joint Normative Deliberation COPAM/CERH n. 01/2008, ‘In special class waters, the natural conditions of the water body must be maintained’.

Time series analysis

The Mann–Kendall nonparametric test was carried out in order to identify the change (increase or decrease) in FCI concentration over time for each monitoring site. Results were shown in terms of percentage of monitoring sites for each WRMU.

Most concentrations in surface waters show strong seasonal patterns (Hirsch et al. 1982; Helsel & Hirsch 2002). Hence, the Kruskal–Wallis nonparametric test was applied initially (α = 5%) to verify whether there are significant differences in FCI concentrations between the monitored semesters. If seasonality were found, then the seasonal Kendall test, which is insensitive to the existence of seasonality (Hirsch et al. 1982), was applied.

The existence of autocorrelation in the data increases the probability of detecting trends when no trends exist, and vice versa (Hamed & Rao 1998; Helsel & Hirsch 2002), so the existence of data autocorrelation was verified. The autocorrelation function measures the degree of correlation of a variable at a given time with itself at a later time (Christofaro & Leão 2009). In the case of data autocorrelation, the modified Mann–Kendall trend test was applied for autocorrelated data, as proposed by Hamed & Rao (1998).

The trend tests result in a p-value, in which at the significance level of 5%, for p > 0.05, there is no trend conclusion; and for p < 0.05, there is a tendency, which may be positive when there is an elevation trend (S > 0) or negative when there is a reduction trend (S < 0) (Hirsch et al. 1982; Yenilmez et al. 2011). Kruskal–Wallis and autocorrelation analysis were applied in the software Statistica 13.0 and trend analysis in XLSTAT 2019.1.3.

RESULTS AND DISCUSSION

Comparison of water quality between sub-basins

The descriptive statistics from the data of each WRMU are shown in Table 3.

Table 3

Descriptive statistics for the FCI concentrations (CFU·100 mL−1) by WRMU

WRMUNo. of dataArithmetic meanStandard deviationGeometric meanMin.10th perc.25th perc.50th perc. (median)75th perc.90th perc.Max.
SF1 463 2,785 11,714 417 50 110 350 1,300 5,000 160,000 
SF2 1,573 23,625 50,780 2,025 100 300 1,700 14,136 90,000 241,960 
SF3 1,908 23,821 46,222 2,830 70 486 4,743 24,000 90,000 241,960 
SF4 938 9,365 31,662 529 30 80 490 2,800 17,000 241,960 
SF5 5,901 39,039 63,612 3,184 50 243 5,000 35,000 160,000 241,960 
SF6 354 12,792 44,127 421 30 70 231 1,700 17,000 241,960 
SF7 1,494 5,638 22,197 392 33 97 290 1,400 8,000 241,960 
SF8 632 6,376 19,832 384 23 58 230 2,300 17,000 160,000 
SF9 782 1,799 11,017 108 30 110 490 2,300 160,000 
SF10 926 8,517 30,458 385 30 80 280 1,572 13,000 241,960 
WRMUNo. of dataArithmetic meanStandard deviationGeometric meanMin.10th perc.25th perc.50th perc. (median)75th perc.90th perc.Max.
SF1 463 2,785 11,714 417 50 110 350 1,300 5,000 160,000 
SF2 1,573 23,625 50,780 2,025 100 300 1,700 14,136 90,000 241,960 
SF3 1,908 23,821 46,222 2,830 70 486 4,743 24,000 90,000 241,960 
SF4 938 9,365 31,662 529 30 80 490 2,800 17,000 241,960 
SF5 5,901 39,039 63,612 3,184 50 243 5,000 35,000 160,000 241,960 
SF6 354 12,792 44,127 421 30 70 231 1,700 17,000 241,960 
SF7 1,494 5,638 22,197 392 33 97 290 1,400 8,000 241,960 
SF8 632 6,376 19,832 384 23 58 230 2,300 17,000 160,000 
SF9 782 1,799 11,017 108 30 110 490 2,300 160,000 
SF10 926 8,517 30,458 385 30 80 280 1,572 13,000 241,960 

After the application of Kruskal–Wallis and multiple comparison tests for FCI concentration data, it was possible to compare the WRMUs, as shown in Figures 3 and 4. It is apparent that the most impacted sub-basins, regarding FCI, are SF3 (Paraopeba River) and SF5 (Velhas River), which did not differ significantly and were higher than all other sub-basins. In addition, SF2 (Pará River) was also considered one of the most impacted sub-basins since its results were higher than all other sub-basins, with the exception of SF3 and SF5. These three WRMUs, which are the most impacted by fecal contamination, cover the Belo Horizonte Metropolitan Region (BHMR) municipalities. Thus, they are the most densely populated of the entire Minas Gerais portion of the São Francisco River basin (Table 1) and have the largest urban areas. Consequently, the contamination by domestic effluents was the most influential factor in these results. Of the total volume of wastewater collected in Brazil, only 10% is treated at a tertiary level (ANA 2012). Therefore, even when treatment is carried out in a given municipality, FCI removal is insufficient.

Figure 3

Box-plot of the FCI concentrations of WRMUs.

Figure 3

Box-plot of the FCI concentrations of WRMUs.

Figure 4

Results of the multiple comparison test following the significant difference detected by the Kruskal–Wallis test, considering the 10 WRMUs. Note: = Group in italic (left column) do not differ significantly (α = 5%) from the group in bold (top). ⇡ Group in italic (left column) significantly (α = 5%) higher than the group in bold (up). ⇣ Group in italic (left column) significantly (α = 5%) lower than the group in bold (top).

Figure 4

Results of the multiple comparison test following the significant difference detected by the Kruskal–Wallis test, considering the 10 WRMUs. Note: = Group in italic (left column) do not differ significantly (α = 5%) from the group in bold (top). ⇡ Group in italic (left column) significantly (α = 5%) higher than the group in bold (up). ⇣ Group in italic (left column) significantly (α = 5%) lower than the group in bold (top).

This outcome is in agreement with the findings of Trindade (2013). The author evaluated the monitoring data of 72 water quality parameters (including thermotolerant coliforms) from SFRB-MG comprising the period spanning 2008–2011. The research showed that, among the 10 WRMUs, SF5 (Velhas River) had the highest water quality degradation by domestic and industrial effluents, and that SF5, SF3 (Paraopeba River), and SF2 (Pará River) showed higher concentrations of thermotolerant coliforms. The concentrations in the three sub-basins were significantly higher than the other sub-basins' concentrations found in the statistical tests, and did not show differences relating to this parameter, which also had the highest violation percentage according to the legislation in all units during the time period in question.

The least impacted sub-basin was SF9 (Pandeiros and Calindó Rivers), that showed a significant difference with a lower median value compared to all others. The results obtained in the tests also corroborated the descriptive statistics, which showed that SF9 had lower median and percentile values compared to the others. This is also shown in Figure 3. This sub-basin has a low population density (Table 1) and is largely composed of a rural population, i.e., 44.6% of rurals among the total inhabitants of the basin in 2010 (IGAM n.d.). Thus, it is possible to affirm that the impact caused by point domestic effluent disposal is considerably lower in the water bodies of the region. The presence of the Pandeiros River Environmental Protection Area (APA), known as the Pantanal of Minas Gerais, is also important. This is the largest sustainable use conservation area in Minas Gerais (IEF 2009). Hence, the expectation of the current study was for lower degradation in terms of fecal contamination.

The same study area (SFRB-MG) was analyzed by Costa et al. (2017) between 2000 and 2013, specifically looking at the violations of 26 water quality parameters when compared to the legislation. FCI represented the highest violation percentage for all WRMUs, except for SF9 (Pandeiros and Calindó Rivers), where it was the fourth most violated parameter. This result was related to the disposal of untreated or improperly treated domestic effluents, as well as to the diffuse pollution from livestock rural drainage.

The violation percentage for each WRMU (Figure 5), considering the limits for each class of their respective monitoring stations, was also calculated. The limits are established by the Joint Normative Deliberation COPAM/CERH n. 01/2008.

Figure 5

Violation percentage of FCI for each WRMU, considering the water bodies' rating from their monitoring sites.

Figure 5

Violation percentage of FCI for each WRMU, considering the water bodies' rating from their monitoring sites.

Notably, the violation percentage was indeed higher in the sub-basins identified as the most impacted by fecal contamination, SF2, SF3, and SF5, and lower in the least impacted, SF9. However, the violation percentage was higher in sub-basin SF2 when compared to SF3 and SF5, with these two showing significantly higher median concentration values than SF2 in the multiple comparison test, as shown in Figures 3 and 4. This may be due to the fact that the sites from Pará River basin violate limits more frequently, and the sites from Paraopeba River basin and Velhas River basin violate the limits by a greater magnitude over the target values.

SF3 and SF5 WRMUs had 30% of monitoring stations with median concentration above 20,000 CFU·100 mL−1. This represents a five times higher concentration than target values for Class 3 water bodies, the least restrictive in the legislation. For SF2, the percentage was 21%. In the other sub-basins (SF1, SF4, SF6, SF7, SF8, SF9, and SF10) the percentage of sites with median FCI concentration above 20,000 CFU·100 mL−1 was below 12%.

Time series analysis

Table 4 shows the results of the Kruskal–Wallis test (comparison between trimesters), i.e., the percentage of sites with seasonality in the period. The percentage of sites that did not show any significant trends and the percentage of sites that showed trends of elevation or reduction in FCI concentration can also be seen.

Table 4

Kruskal–Wallis and Mann–Kendall/seasonal Kendall test results in various WRMUs

WRMU% Sites with seasonality% Sites – no significant trend% Sites – significant trend
ElevationReduction
SF1 71% 86% 0% 14% 
SF2 44% 88% 13% 0% 
SF3 48% 71% 14% 14% 
SF4 100% 86% 14% 0% 
SF5 37% 75% 8% 17% 
SF6 100% 75% 25% 0% 
SF7 88% 88% 13% 0% 
SF8 75% 100% 0% 0% 
SF9 71% 86% 0% 14% 
SF10 50% 75% 0% 25% 
WRMU% Sites with seasonality% Sites – no significant trend% Sites – significant trend
ElevationReduction
SF1 71% 86% 0% 14% 
SF2 44% 88% 13% 0% 
SF3 48% 71% 14% 14% 
SF4 100% 86% 14% 0% 
SF5 37% 75% 8% 17% 
SF6 100% 75% 25% 0% 
SF7 88% 88% 13% 0% 
SF8 75% 100% 0% 0% 
SF9 71% 86% 0% 14% 
SF10 50% 75% 0% 25% 

Table 4 shows that SF2, SF3, and SF5 WRMUs, identified as the most impacted in terms of FCI concentration, have low percentage of sites with seasonality in the period. These WRMUs cover the BHMR municipalities, so they are densely populated and fecal contamination mainly arises from point source pollutions through wastewater disposal. In other sub-basins, there must be non-point source pollutions related to livestock activity, which results in higher FCI concentration during the rainy season (first and fourth semesters in the region) through rainfall runoff.

The influence of seasonality on FCI concentration was identified in several watersheds, in different countries in different climates (Schilling et al. 2009; Daly et al. 2013; Laurent & Mazumder 2014; Tornevi et al. 2014; Rochelle-Newall et al. 2016). Seasonal variability is more strongly associated with runoff in catchments where contamination is related to diffuse fecal sources (Laurent & Mazumder 2014). In more urbanized regions, high variability in FCI concentration is mainly verified through point-source contamination, which is often unrelated to rainfall runoff (Schilling et al. 2009; Daly et al. 2013; Laurent & Mazumder 2014).

Regarding the results of the trend test for all WRMUs, as shown in Table 4, most monitoring stations showed no significant trend. Although Costa et al. (2017) identified the FCI parameter as the one related to more monitoring stations with significant elevation trends in violation percentages, they also found that most stations presented no trend for all WRMUs. According to the authors, the results are alarming, as they show the maintenance of a degradation state in water bodies from SFRB-MG.

CONCLUSIONS

Following the evaluations made for the FCI data retrieved from IGAM between 2000 and 2018, it is concluded that there is still a precarious situation of basic sanitation in the 10 sub-basins of the area. Despite the great socioeconomic importance of the area for Brazil and the use of water resources for many activities, water bodies find themselves degraded by fecal contamination in the region where the river starts and where there is a large part of the basin population, mainly due to domestic effluent disposal.

The most affected WRMUs regarding fecal contamination were SF5 (Velhas River) and SF3 (Paraopeba River), followed by SF2 (Pará River). These are the densest regions of the entire study area, comprising the BHMR sub-basins. Thus, the release of untreated or insufficiently treated sewage was the main factor influencing the results. The most impacted WRMUs were also those showing higher proportions of monitoring sites with median FCI concentration values above 20,000 CFU·100 mL−1.

On the other hand, the least impacted WRMU in terms of fecal contamination was SF9 (Pandeiros and Calindó Rivers). This sub-basin has a low population density and contains the important Pandeiros River Environmental Protection Area. Thus, a lower FCI concentration was already expected.

It can be concluded based on the findings that in urbanized areas, fecal contamination is mainly from point sources, while in less dense areas, diffuse pollution can also contribute to contamination, with a more seasonal influence. For most monitoring stations in all WRMUs there was no trend in concentrations over time, indicating the maintenance of a degradation state in water quality in SFRB-MG.

It is necessary to invest in actions, projects, and public policies involving sanitation in the municipalities of this important Brazilian region, in order to ensure proper collection and treatment of wastewater, reducing risks to public health and assuring higher environmental preservation.

ACKNOWLEDGEMENTS

The authors would like to thank the Instituto Mineiro de Gestão das Águas (IGAM) for providing the monitoring data, Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for their financial support during the course of the research. The funding sources had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

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