Water quality is one of the world's greatest concerns as it is a fundamental resource for life, and it must constantly be monitored. This study aimed to carry out seasonal monitoring of water quality in the hydrographic basin of the Urutu-MS stream, which is occupied by more than 50% of eucalyptus silviculture forests. Water samples were collected from five points seasonally between autumn 2019 and summer 2020. In total, twenty-three parameters were included, including temperature (Cº), pH, Dissolved Oxygen (DO, mg/L), Conductivity (EC ), Total Alkalinity (ALK), Organic Matter (OM), Chloride (Cl), Sulfate (SO42), Total Iron (Fe Total), Color, Turbidity (NTU), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Phosphorus (PT), Hardness, Aluminum (Al), Manganese (Mn), Magnesium (Mg), Sodium (Na), Calcium (Ca), Copper (Cu) and Dissolved Iron (Fe). The results showed that there is a difference in water quality between the seasons of the year depending on the different concentrations of physical-chemical parameters. Among the seasons, spring was classified in class II, while the others were classified in class I, showing the influence of the onset of rains after the dry winter. Interactions between parameters also followed conventional associations such as ALK and EC, TDS and EC, Color and Turbidity, TSS and Turbidity, EC and Ca, EC and Mg. The ALK and TDS were the main parameters that showed an influence of seasonality.
Research Article
Seasonal analysis of aquatic water quality in a forestry area, MS, Brazil
https://doi.org/10.21203/rs.3.rs-2600309/v1
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Paraná Watershed
Land use and Land Cover
Cerrado
Silviculture
Seasonality
Water quality is one of the most important global topics (Bilgin, 2018; Zhang et al., 2018; Abbott et al., 2019; Gaytán-Alarcón et al., 2022) due to its numerous functionalities. Human consumption, agriculture, animal watering, industrial use and mainly environmental conservation can be mentioned (Shi et al., 2017; Zhang et al., 2019; Gomes et al., 2020) as water can show changes in the environment, constituting a necessary tool for environmental monitoring.
In this context, water quality monitoring is an essential tool used in several studies (Andrietti et al., 2016; Amâncio et al., 2018; Soldán, 2021; Oliveira et. al., 2022; Giao, 2022) to control potential risks to human health (Islam et al., 2020), as well as in environmental assessment, also functioning as a water resource management tool.
According to Tucci (1997), water quality is influenced by numerous factors, such as geology, soil, land use and land cover, geomorphology and climate, and each water system has its particularities. Li et al. (2015) point out that the influence of climatic seasonality, due to different rainfall patterns, can affect water quality, which is corroborated by several studies in the academic literature (Shi et al., 2017; Zhang et al., 2017; Bhatti et al., 2018; Mohseni-Bandpei et al., 2018; Yevenes et al., 2018; Wei et al., 2018). In addition, there is evidence indicating that seasons have specific characteristics that can disturb surface water quality (Zhong et al., 2018).
Thus, water quality reflects the characteristics and interactions of the environment (Hung et al., 2020; Xu et al., 2020), as there is evidence that water quality in watersheds can be affected by the disposition of vegetation cover (Shi et al., 2017; Gomes et al., 2020). Therefore, water quality analysis is an important tool for environmental planning of watersheds (Fu et al., 2022).
Thus, environmental changes provided by land use and land cover can bring numerous social and economic benefits but can lead to numerous disturbances in the components of the hydrological cycle (Taniwaki et al., 2017; Birhanu et al., 2019). Land use can cause the displacement of numerous chemical and physical elements to water bodies (Pacheco and Fernandes, 2016; Gomes et al., 2020).
Considering this, in an attempt to guarantee not only the water quality and quantity, the National Water Resources Policy was instituted in Brazil under Law 9.433/97 (Brasil, 1997) to ensure the availability of water for current and future generations. Another concern in this regard refers to the establishment of water quality standards, which created a legal instrument through the Ministry of the Environment (MMA), via the National Environment Council (CONAMA), which developed Resolution No. 357 of 17 March 2005, functioning as a guideline in the assessment of water quality conditions (Brasil, 2005). There are several studies in the literature that demonstrate the satisfactory applicability of this resolution as a parameter in monitoring water quality (Lelis et al., 2015; Thompson et al., 2020; Berlanda et al., 2021). However, there is a gap in the literature focusing on seasonal monitoring of surface water quality in watersheds in areas heavily occupied by eucalyptus trees.
Thus, the present work aims to carry out the seasonal monitoring of the surface water quality of the Urutu watershed (UW) in the municipality of Aparecida do Taboado-MS, Brazil, based on the parameters established by the CONAMA Resolution 357/2005 (Brasil, 2005), followed by CECA/MS Deliberation No. 36/2012 (Mato Grosso do Sul, 2012) and CERH/MS Resolution No. 52/2018 (Mato Grosso do Sul, 2018), as a subsidy for the implementation of monitoring local seasonality of water quality, as well as enabling the construction of an additional component variable in future studies of environmental fragility analysis.
Study Area
The Urutu watershed (UW), located in the municipality of Aparecida do Taboado, east of the state of Mato Grosso do Sul, is an important eucalyptus/cellulose producing region in Brazil. The UW has an approximate area of 99.45 km², between the parallels of 20°02'26” S and 20°10'22” S and the meridians of 51° 36'86”W and 51°27'24” W ( Fig. 1).
The UW is based on the lithologies of the Bauru Group, including the Santo Anastácio Formation, occupying an area of 23.06%, and the Caiuá Group, with the Vale do Rio do Peixe Formation, with an area of 76.94%, both deposited on basalts from the Serra Geral Formation (Fernandes and Coimbra, 2000). The areas of soil present in the basin are: 48.79% of Red Oxisol (LV), 46,15% de Red Ultisol (PV) and 5.05% de Alfisol Haplic (SX).
The climate in the region of Aparecida do Taboado is classified as Aw (Alvares et al., 2013) and has characteristics of a tropical climate with a dry season in the winter. It has a rainfall regime characterized by a historical average annual rainfall of 1,332.6 mm between 1983 and 2016 (Figs. 2 and 3), according to data from the rainfall stations of the Agência Nacional Águas e Saneamento Básico (ANA) (Ana, 2019) and the Canal CLIMA of UNESP Ilha Solteira (Canal Clima, 2019).
Metodology
The methodological procedures adopted were based on the proposed classification of surface fresh water quality governed by CONAMA Resolution 357/2005 (Brasil, 2005) and CECA Deliberation 36/2012, evaluated by season, comprising autumn 2019, winter 2019, spring 2019 and summer 2020. The steps taken can be verified according to the scheme shown in the flowchart below (Fig. 4).
The criteria considered for the selection of surface water quality monitoring points were: river confluences, forms of use and land cover and accessibility. In total, five monitoring points were selected (Fig. 5 and Table 1).
Table 1 - Surface water monitoring points.
|
Collection points |
Coordinates |
Land use and land cover (occupied area in %) |
|
1 |
51°30'19,662"W 20°5'22,376"S |
74 of pasture; 22.12 forestry; 2.67 riparian vegetation; 1 water. |
|
2 |
51°29'32,939"W 20°6'21,841"S |
97.32 pasture; 2.68 riparian vegetation. |
|
3 |
51°32'25,267"W 20°6'52,181"S |
47.6 pasture; 43.03 forestry; 7.62 riparian vegetation; 1.49 exposed soil. |
|
4 |
51°33'12,596"W 20°7'16,452"S |
16.98 pasture; 61.39 forestry; 9.71 riparian vegetation; 10.42 closed; 1.50 exposed soil. |
|
5 |
51°33'55,678"W 20°9'16,596"S |
18.24% pasture; 70.59 forestry; 7.71 riparian vegetation; 1.38 closed; 2.07 exposed soil. |
The water quality parameters are based on CONAMA Resolution 357/2005, CECA Deliberation 36/2012 and Resolution 52/2018 CERH/MS which provides a classification of water bodies, establishing five quality classes/framework.
Water collected in the field (simple samples) were carried out between 9 am and 1 pm, collected in 1 liter threaded amber bottles, previously sterilized and prepared with preservatives and stored in a refrigerated isothermal box, at a depth of 20 cm. The collection interval was three months, taking place in: May 2019, August 2019, November 2019 and February 2020, accounting for four collections comprising the seasons. The samples were collected and packaged according to the recommendations of the Guia Nacional de Coleta e Preservação de Amostras (Cetesb, 2011), and processed following the Standard Methods for the Examination of Water and Wastewater standards (Apha, 2005). The parameters are shown in Table 2 containing the equipment and analysis methods applied to the water samples.
Table 2 - Equipment and methods for analyzing surface water quality parameters.
|
Parameter |
Unit |
Equipment |
Method |
|
Temperature |
°C |
Thermometer (Horiba 50) |
electrometric |
|
pH |
|
pH phmetro meter (Mettler Toleto) |
potentiometric |
|
Dissolved Oxygen (DO) |
mg/L |
Multiparameter Meter (Horiba 50) |
spectrophotometric |
|
Electric Conductivity (EC) |
µs/cm-1 |
Multiparameter Meter (Horiba 50) |
amperometric |
|
Total Alkalinity (ALK) |
mg CaCO3 L−1 |
Titrine (Metrohm) |
titration |
|
Organic Matter (OM) |
mg/L |
Titrine (Metrohm) |
titration |
|
Chloride (Cl-) |
mg/L |
Liquid Chromatograph (Metronhm) |
chromatography |
|
Sulfate (SO4-2) |
mg/L |
Liquid Chromatograph (Metronhm) |
chromatography |
|
Total Iron (Total Fe) |
mg/L |
DR 3900 |
spectrophotometric |
|
Color |
Pt/Co |
DR 3900 |
spectrophotometric |
|
Turbidity (NTU) |
NTU |
Turbidimeter (Horiba 50) |
spectrophotometric |
|
Total Dissolved Solids (TDS) |
mg/L |
Multiparameter Meter (Mettler Toleto) |
gravimetric |
|
Total Suspended Solids (TSS) |
mg/L |
Filtration Kit |
gravimetric |
|
Chemical Oxygen Demand (COD) |
mg/L |
DR 3900 |
spectrophotometric |
|
Total Phosphorus (TP) |
mg/L |
DR 3900 |
spectrophotometric |
|
Hardness |
mg/L |
DR 3900 |
spectrophotometric |
|
Aluminum (Al) |
mg/L |
Atomic Absorption (Perkin Elmer) |
spectrophotometric |
|
Manganese (Mn) |
mg/L |
Atomic Absorption (Perkin Elmer) |
spectrophotometric |
|
Magnesium (Mg) |
mg/L |
Atomic Absorption (Perkin Elmer) |
spectrophotometric |
|
Sodium (Na) |
mg/L |
Flame Photometer |
spectroscopy |
|
Calcium (Ca) |
mg/L |
Atomic Absorption (Perkin Elmer) |
spectrophotometric |
|
Copper (Cu) |
mg/L |
Atomic Absorption (Perkin Elmer) |
spectrophotometric |
|
Dissolved Iron (Fe) |
mg/L |
Atomic Absorption (Perkin Elmer) |
spectrophotometric |
Next, the average classification was performed by UW parameter, in which the sum of classes (Special (0), Class I (1), Class II (2), Class III (3), Class IV (4)) participated by the variable divided by the number of collection points. A general classification of water quality for the basin was also applied, based on the sum of the average classes divided by the number of sample points. After classification, Pearson's statistical correlation test was applied between the monitored parameters.
Monitoring of surface water quality in autumn 2019
In autumn, the general classification for this season was in class I (Table 3), which precedes the dry period, and is characterized by the beginning of a decrease in the volume of precipitation, also presenting the highest vegetative index and better water quality among all seasons. (Fig. 3). These results corroborate what was observed in other studies that found an improvement in water quality related to the presence of vegetation cover (Wang et al., 2014; Ding et al., 2015; Zhang et al., 2018).
Table 3 - Analytical results of the physical and chemical parameters of surface water and their classification according to Resolution 357/2005 CONAMA at UW in autumn 2019.
|
Parameters analyzed |
Unit of measure |
Monitoring Points |
Classification by parameter |
|||||
|
4 |
5 |
6 |
7 |
8 |
||||
|
Physical Parameters Parâmetros físicos |
Temperature |
°C |
23 |
23 |
23 |
23 |
23 |
|
|
Color |
Pt/Co |
41 |
78 |
266 |
52 |
79 |
II |
|
|
Turbidity |
NTU |
1.49 |
5.99 |
30.5 |
4.48 |
8.9 |
S |
|
|
TDS |
mg/L |
6.16 |
21.8 |
6.84 |
8.06 |
9.1 |
I |
|
|
TSS |
mg/L |
3 |
8 |
101 |
1 |
7 |
|
|
|
Chemical Parameters
|
pH |
|
7.05 |
6.7 |
6.12 |
6.38 |
6.76 |
I |
|
DO |
mg/L |
10.5 |
7.44 |
6.28 |
8.62 |
11.93 |
I |
|
|
EC |
µS/cm |
12.54 |
44 |
14.41 |
19.56 |
18.69 |
S |
|
|
ALK |
Ppm |
8.2 |
19.2 |
7 |
9.8 |
9.6 |
|
|
|
OM |
Ppm |
0.88 |
2.16 |
24 |
1.68 |
1.04 |
|
|
|
Cl- |
Ppm |
0.43 |
1.508 |
0.253 |
0.256 |
0 |
I |
|
|
SO4-2 |
Ppm |
0 |
0 |
0 |
0 |
0 |
S |
|
|
Total Fe |
Ppm |
0.222 |
0.0111 |
0.215 |
0.387 |
0.305 |
|
|
|
COD |
mg/L |
25.96 |
31.18 |
12.56 |
0.79 |
7.54 |
|
|
|
TP |
Ppm |
0.01 |
0.01 |
0.02 |
0.01 |
0.01 |
I |
|
|
Hardness |
Ppm |
12 |
14 |
9 |
5 |
8 |
|
|
|
Al |
Ppm |
0.05 |
0 |
0.23 |
0 |
0 |
I |
|
|
Mn |
Ppm |
0 |
0.39 |
0 |
0 |
0 |
I |
|
|
Mg |
Ppm |
0.79 |
2.55 |
0.88 |
0.95 |
1.01 |
|
|
|
Na |
Ppm |
0 |
1 |
0 |
0 |
0 |
|
|
|
Ca |
Ppm |
2.96 |
4.91 |
3.4 |
2.91 |
2.98 |
|
|
|
Cu |
Ppm |
0.04 |
0.05 |
0.04 |
0.05 |
0.04 |
|
|
|
Fe |
Ppm |
0.57 |
0.13 |
0.85 |
0.43 |
0.35 |
III |
|
|
Average rating per point |
I |
I |
I |
I |
I |
Autumn Overall Ranking I |
||
The turbidity presented classification in all points, as well as in general, in the Special class (0 to 20). In the correlation matrix, a trend of positive correlation was observed, TSS, TDS and OM. At point 3, turbidity and TSS showed concentrations that may be related to the topographic factor, as it is an aggradation area, characterized by sediment deposition and an increase in organic and inorganic matter in the water (Chen and Chau, 2016). Turbidity is closely associated with rain (Silva, 2013) and TSS, as the main factor responsible for turbidity is TSS (Gilvear and PettS, 1985; Grayson et al., 1996; Hannouche et al., 2011).
The TDS was within the limits of class I (200 to 300) at all sampling points. The correlation matrix showed a trend of positive correlation with EC, corroborating what was found by Atekwana et al. (2004), and with conductive ions such as Ca, Cl- and ALK (Table 4).
The color presented a classification in class II (<75) at points 2, 3 and 5 and class I at points 1 and 4. This parameter shows a tendency of positive correlation with TSS, OM, which was observed by Macedo (2004), and turbidity, as well as dissolved substances (Rocha and Costa, 2015), such as iron, Al and TP.
The pH and the DO presented a general classification in class I. The stability of these parameters is associated with the high levels of vegetal biomass, providing protection of the soil and water resources, and with the decrease of the precipitation.
Table 4 - Correlation matrix of physical and chemical parameters of surface water at UW in autumn 2019
|
|
DO |
pH |
EC |
ALK |
OM |
Cl |
Fe total |
Cor |
Turbidity |
TDS |
TSS |
COD |
TP |
Hardness |
Al |
Mg |
Ca |
Cu |
|
pH |
0.70 |
|||||||||||||||||
|
EC |
-0.36 |
0.09 |
||||||||||||||||
|
ALK |
-0.27 |
0.22 |
0.99 |
|||||||||||||||
|
OM |
-0.64 |
-0.77 |
-0.28 |
-0.39 |
||||||||||||||
|
Cl |
-0.48 |
0.19 |
0.90 |
0.91 |
-0.19 |
|||||||||||||
|
Fe total |
0.43 |
-0.20 |
-0.74 |
-0.74 |
-0.08 |
-0.88 |
||||||||||||
|
Cor |
-0.60 |
-0.76 |
-0.21 |
-0.33 |
0.99 |
-0.17 |
-0.12 |
|||||||||||
|
Turbidity |
-0.55 |
-0.78 |
-0.25 |
-0.37 |
0.97 |
-0.24 |
-0.05 |
0.99 |
||||||||||
|
TDS |
-0.33 |
0.13 |
0.99 |
0.99 |
-0.27 |
0.91 |
-0.79 |
-0.19 |
-0.23 |
|||||||||
|
TSS |
-0.60 |
-0.74 |
-0.28 |
-0.39 |
1.00 |
-0.19 |
-0.10 |
0.99 |
0.98 |
-0.26 |
||||||||
|
COD |
-0.18 |
0.53 |
0.51 |
0.57 |
-0.12 |
0.78 |
-0.90 |
-0.12 |
-0.21 |
0.58 |
-0.10 |
|||||||
|
TP |
-0.61 |
-0.75 |
-0.33 |
-0.43 |
1,00 |
-0.22 |
-0.05 |
0.98 |
0.97 |
-0.31 |
1.00 |
-0.13 |
||||||
|
Hardness |
-0.16 |
0.51 |
0.53 |
0.58 |
-0.09 |
0.77 |
-0.92 |
-0.06 |
-0.14 |
0.60 |
-0.06 |
0.99 |
-0.10 |
|||||
|
Al |
-0.54 |
-0.62 |
-0.43 |
-0.51 |
0.97 |
-0.24 |
-0.06 |
0.93 |
0.91 |
-0.40 |
0.97 |
-0.04 |
0.98 |
-0.01 |
||||
|
Mg |
-0.38 |
0.12 |
0.99 |
0.98 |
-0.23 |
0.93 |
-0.82 |
-0.16 |
-0.20 |
1.00 |
-0.22 |
0.61 |
-0.27 |
0.64 |
-0.35 |
|||
|
Ca |
-0.55 |
-0.01 |
0.93 |
0.90 |
0.02 |
0.94 |
-0.91 |
0.08 |
0.02 |
0.94 |
0.02 |
0.69 |
-0.02 |
0.71 |
-0.09 |
0.96 |
||
|
Cu |
-0.42 |
-0.16 |
0.71 |
0.70 |
-0.36 |
0.61 |
-0.19 |
-0.38 |
-0.40 |
0.64 |
-0.41 |
0.03 |
-0.41 |
-0.03 |
-0.51 |
0.63 |
0.51 |
The EC classified in the Special class (<50) showed a positive correlation trend with ALK, Cl, Mg and Ca, which are parameters associated with EC. The highest occurrence of EC was detected at point 2 and may be related to livestock activities, combined with the elevation of dissolved ion concentration (Anjinho et al., 2020).
PT, classified as class I (<0.020), showed a trend of positive correlation with turbidity and, mainly, with OM and TSS (Table 4). The reduction in total P concentration may be related to the vegetation cover, as riparian vegetation contributes to the reduction of phosphorus input into the water body (Neilen et al., 2017).
Al presented a value of <0.1 and was occasionally categorized in point 1 as class I. Between points 2, 4 and 5, the Special class (0) was identified and in point 3, Class III (<0.2), with the same classification for Fe. Both parameters showed a positive correlation with color, turbidity and TSS. Occasionally, points 1 and 3 have the highest concentrations of Fe, with a positive correlation trend, not only with color, turbidity and TSS, but also with OM and TDS, whose association of Fe with OM can increase the color (Richter and Netto, 1991). This Fe concentration may also be associated with the geology of the basin, as they are based on the Santo Anastácio Formation, which contains a thin layer of iron in its composition (Ipt, 1981).
Monitoring of surface water quality in the winter of 2019
In winter, the general classification for this station was in class I, although higher concentrations individually between some parameters are already presented in the other points, such as pH, Fe and Al (Table 5).
Table 5 - Analytical results of physical and chemical parameters of surface waters and their classification according to Resolution 357/2005 CONAMA of UW in winter.
|
Parameters analyzed |
Unit of measure |
Monitoring points |
Classification by parameter |
|||||
|
4 |
5 |
6 |
7 |
8 |
||||
|
Physical Parameters |
Temperature |
°C |
23 |
* |
23 |
23 |
23 |
|
|
Color |
Pt/Co |
192 |
* |
45 |
43 |
43 |
I |
|
|
Turbidity |
NTU |
21.4 |
* |
7.36 |
3.19 |
5.66 |
S |
|
|
TDS |
mg/L |
9.49 |
* |
11.19 |
8.8 |
11.29 |
I |
|
|
TSS |
mg/L |
3 |
* |
2 |
0 |
4 |
|
|
|
Chemical Parameters
|
pH |
|
6.59 |
* |
5.64 |
5.91 |
6.36 |
III |
|
DO |
mg/L |
9.62 |
* |
10.5 |
10.54 |
10.43 |
S |
|
|
EC |
µS/cm |
18.97 |
* |
22.4 |
17.6 |
22.6 |
S |
|
|
ALK |
Ppm |
11.1 |
* |
12.1 |
11.2 |
10.1 |
|
|
|
OM |
Ppm |
2.88 |
* |
2.24 |
1.44 |
0.72 |
|
|
|
Cl- |
Ppm |
0.861 |
* |
0.341 |
0 |
0 |
I |
|
|
SO4-2 |
Ppm |
0.85 |
* |
0.168 |
0.129 |
0.149 |
I |
|
|
Fe total |
Ppm |
0.616 |
* |
0.657 |
0.417 |
0.473 |
|
|
|
COD |
mg/L |
11.75 |
* |
6.33 |
0 |
0 |
|
|
|
TP |
Ppm |
0.03 |
* |
0 |
0 |
0 |
I |
|
|
Hardness |
Ppm |
19 |
* |
8 |
7 |
7 |
|
|
|
Al |
Ppm |
2.13 |
* |
2.31 |
1.43 |
1.53 |
IV |
|
|
Mn |
Ppm |
0 |
* |
0 |
0 |
0 |
|
|
|
Mg |
Ppm |
0.6 |
* |
0.27 |
0.98 |
0.96 |
|
|
|
Na |
Ppm |
1 |
* |
1 |
1 |
1 |
|
|
|
Ca |
Ppm |
2.18 |
* |
2.19 |
1.95 |
2 |
|
|
|
Cu |
Ppm |
0 |
* |
0 |
0 |
0 |
|
|
|
Fe |
Ppm |
1.62 |
* |
4.36 |
0.52 |
0.57 |
III |
|
|
Average rating per point |
I |
* |
I |
I |
I |
General Rating Winter I |
||
*dry spot.
Turbidity was present in the Special class, mainly due to low precipitation, as what occurred in autumn. This also showed a trend of positive correlation with SST (Table 6).
Color also presented a general classification in class I, except for point 1 in class II. There is a trend towards a positive correlation between this parameter and sulfate, as it is involved in the oxidation of organic matter (Ribeiro, et al., 2016), since in the process of degradation of organic matter there is a release of humic and phenolic acids responsible for increasing the color (Richter and Netto, 1991).
The TDS were identified as class I for all points, showing a trend of positive correlation with EC (Table 6), due again to the lower input of water from precipitation in the system.
Table 6 - Correlation matrix of physical and chemical parameters of surface water at UW in the winter of 2019.
|
|
DO |
pH |
EC |
ALK |
OM |
Cl- |
SO4-2 |
Total Fe |
Color |
Turbidity |
TDS |
TSS |
COD |
TP |
Hardness |
Al |
Mg |
Ca |
|
pH |
-0.77 |
|||||||||||||||||
|
EC |
0.30 |
-0.16 |
||||||||||||||||
|
ALK |
0.09 |
-0.70 |
-0.08 |
|||||||||||||||
|
OM |
-0.71 |
0.09 |
-0.29 |
0.64 |
||||||||||||||
|
Cl- |
-0.91 |
0.45 |
-0.18 |
0.31 |
0.92 |
|||||||||||||
|
SO4-2 |
-1.00 |
0.71 |
-0.34 |
0.00 |
0.77 |
0.93 |
||||||||||||
|
Total Fe |
-0.44 |
-0.07 |
0.38 |
0.61 |
0.78 |
0.75 |
0.48 |
|||||||||||
|
Color |
-0.99 |
0.72 |
-0.37 |
-001 |
0.76 |
0.92 |
1.00 |
0.45 |
||||||||||
|
Turbidity |
-0.98 |
0.67 |
-0.20 |
0.05 |
0.79 |
0.97 |
0.99 |
0.60 |
0.98 |
|||||||||
|
TDS |
0.30 |
-0.16 |
1.00 |
-0.08 |
-0.29 |
-0.17 |
-0.34 |
0.38 |
-0.37 |
-0.19 |
||||||||
|
TSS |
-0.39 |
0.62 |
0.67 |
-0.53 |
-0.08 |
0.27 |
0.31 |
0.32 |
0.29 |
0.40 |
0.67 |
|||||||
|
COD |
-0.84 |
0.33 |
-0.09 |
0.42 |
0.94 |
0.99 |
0.87 |
0.83 |
0.86 |
0.92 |
-0.09 |
0.25 |
||||||
|
TP |
-0.99 |
0.72 |
-0.38 |
-0.02 |
0.75 |
0.92 |
1.00 |
0.44 |
1.00 |
0.98 |
-0.38 |
0.29 |
0.85 |
|||||
|
Hardness |
-0.99 |
0.68 |
-0.34 |
0.05 |
0.80 |
0.95 |
1.00 |
0.51 |
1.00 |
0.99 |
-0.34 |
0.29 |
0.89 |
1.00 |
||||
|
Al |
-0.42 |
-0.14 |
0.30 |
0.69 |
0.82 |
0.75 |
0.47 |
0.99 |
0.44 |
0.58 |
0.31 |
0.22 |
0.84 |
0.43 |
0.50 |
|||
|
Mg |
0.19 |
0.38 |
-0.37 |
-0.80 |
-0.71 |
-0.57 |
-0.24 |
-0.95 |
-0.22 |
-0.37 |
-0.37 |
-0.08 |
-0.69 |
-0.20 |
-0.28 |
-0.97 |
||
|
Ca |
-0.54 |
0.01 |
0.29 |
0.59 |
0.83 |
0.82 |
0.58 |
0.99 |
0.55 |
0.69 |
0.29 |
0.32 |
0.89 |
0.54 |
0.61 |
0.99 |
-0.92 |
|
|
Fe |
0.07 |
-0.58 |
0.46 |
0.83 |
0.54 |
0.34 |
-0.02 |
0.85 |
-0.04 |
0.12 |
0.46 |
-0.01 |
0.48 |
-0.05 |
0.03 |
0.87 |
-0.97 |
0.79 |
The pH specifically presented a classification difference, point 1, located upstream, and point 5, close to the mouth, which were classified in class I; points 3 and 4 have a slightly acidic pH, categorized in class IV, which can be explained by the decomposition of the accumulation of leaves, vegetation branches, organic matter, resulting in the release of humic acid (Anjinho et al., 2020) from the parts highest on the terrain (headwaters). It should be noted that point 3 is in a sediment deposition area, therefore TSS tends to accumulate in this dry period, increasing concentration and decreasing transport (Stevaux and Latrubresse, 2017), which corroborates with what is indicated in the correlation matrix which showed a trend of positive correlation with the TSS, turbidity, color and SO42. The OD was generally in class I (>6 <10), and point 1 was the only one at this station classified in class I, due to a small variation in the concentration value, in which the other points were within the limits of the special class (>10).
As in autumn, EC in winter remained in the Special class. In this season, there was also a trend towards a positive correlation between EC and TDS due to the lower precipitation rate, limiting the entry of material through the runoff.
The Cl- was present in class I (<250), in points 1 and 3, and was absent in points 4 and 5. SO4-2, as well as Cl-, were present in class I, differently from autumn, where its occurrence was not identified. Both parameters tend to have a positive correlation with OM due to the dissolution of these elements in substances that compose these parameters.
Fe predominated in class III (<5.0), with the highest concentrations in points 1 and 3, which, as in the previous season, showed a trend of positive correlation with OM and TDS.
Al showed higher concentrations at all points, classified in class IV (>0.2), and similar to Fe, this element also showed a positive correlation with OM.
Monitoring of surface water quality monitoring in spring 2019
In spring it was found that point 2 was still dry, resulting from the removal of riparian vegetation. In the basin region, spring is characterized by the beginning of the rainy season, with a large accumulation of litter from the vegetation itself, as a result of previous seasons (Inkotte et al., 2022). According to data analysis, in general, all parameters showed an increase in their indices (Table 7).
Table 7 - Analytical results of physical and chemical parameters of surface waters and their classification according to Resolution 357/2005 CONAMA of UW in Spring
|
Parameters analyzed |
Unit of measure |
Monitoring points |
Classification by parameter |
|||||
|
4 |
5 |
6 |
7 |
8 |
||||
|
Physical Parameters |
Temperature |
°C |
20 |
* |
20 |
20 |
20 |
|
|
Color |
Pt/Co |
579 |
* |
637 |
99 |
71 |
II |
|
|
Turbidity |
NTU |
49.4 |
* |
85.7 |
4.94 |
7.95 |
I |
|
|
TDS |
mg/L |
11.76 |
* |
4.67 |
7.29 |
9.53 |
I |
|
|
TSS |
mg/L |
13 |
* |
0 |
0 |
1 |
|
|
|
Chemical Parameters
|
pH |
|
7.08 |
* |
5.53 |
6.13 |
6.62 |
II |
|
DO |
mg/L |
8.78 |
* |
8.52 |
9.5 |
10.62 |
I |
|
|
EC |
µS/cm |
23.5 |
* |
9.53 |
14.58 |
19.06 |
S |
|
|
ALK |
Ppm |
8.6 |
* |
5.9 |
9.1 |
9.9 |
|
|
|
OM |
Ppm |
4.64 |
* |
4.48 |
0 |
0 |
|
|
|
Cl- |
Ppm |
2.152 |
* |
0.472 |
0 |
0 |
I |
|
|
SO4-2 |
Ppm |
0.301 |
* |
0.897 |
0.202 |
0.127 |
I |
|
|
Total Fe |
Ppm |
4.85 |
* |
1.574 |
1.706 |
0.739 |
|
|
|
COD |
mg/L |
74.28 |
* |
9.92 |
21.06 |
0 |
|
|
|
TP |
Ppm |
0.07 |
* |
0.06 |
0 |
0 |
I |
|
|
Hardness |
Ppm |
1 |
* |
3 |
26 |
8 |
|
|
|
Al |
Ppm |
4.34 |
* |
11.08 |
0.58 |
0.82 |
IV |
|
|
Mn |
Ppm |
0 |
* |
0 |
0 |
0 |
- |
|
|
Mg |
Ppm |
0.75 |
* |
0.75 |
0.78 |
1.06 |
|
|
|
Na |
Ppm |
1 |
* |
1 |
0 |
0 |
|
|
|
Ca |
Ppm |
3.16 |
* |
2.4 |
3.26 |
4.03 |
|
|
|
Cu |
Ppm |
0.03 |
* |
0.13 |
0.03 |
0.03 |
|
|
|
Fe |
Ppm |
4.25 |
* |
1.83 |
1.5 |
1.21 |
III |
|
|
Average rating per point |
II |
|
II |
I |
I |
Spring Overall Ranking II |
||
* dry spot.
The turbidity showed variations in the concentration between the sampling points, as well as in other works (Antunes et al., 2014; Rossiter et al., 2015; Amorim et al., 2017), with points 1 and 3 in class II ( 40 to 70) and points 4 and 5 in the special class.
Color proved to be general in class 2 (>75), and occasionally, except for point 4 in class I, the others were classified in class II. It can be observed that there was an increase in the concentration of this parameter in relation to previous seasons, mainly due to the increase in the concentration of dissolved substances such as sulfate, Fe and even OM, registering a trend of positive correlation between these elements (Table 8).
Table 8- Correlation matrix of physical and chemical parameters of surface water at UW in spring 2019.
|
|
OD |
pH |
EC |
AKL |
OM |
Cl- |
SO4-2 |
Total Fe |
Color |
Turbidity |
TDS |
TSS |
COD |
TP |
Hardness |
Al |
Mg |
Ca |
Cu |
|
pH |
0.31 |
|
|||||||||||||||||
|
EC |
0.28 |
1.00 |
|
||||||||||||||||
|
ALK |
0.81 |
0.71 |
0.68 |
|
|||||||||||||||
|
OM |
-0.86 |
-0.04 |
-0.01 |
-0.73 |
|
||||||||||||||
|
Cl- |
-0.57 |
0.59 |
0.62 |
-0.13 |
0.76 |
|
|||||||||||||
|
SO4-2 |
-0.74 |
-0.73 |
-0.71 |
-0.99 |
0.70 |
0.06 |
|
||||||||||||
|
Total Fe |
-0.57 |
0.60 |
0.62 |
-0.05 |
0.65 |
0.97 |
-0.04 |
|
|||||||||||
|
Color |
-0.89 |
-0.15 |
-0.12 |
-0.80 |
0.99 |
0.69 |
0.77 |
0.58 |
|
||||||||||
|
Turbidity |
-0.83 |
-0.41 |
-0.39 |
-0.93 |
0.91 |
0.42 |
0.92 |
0.29 |
0.95 |
|
|||||||||
|
TDS |
0.29 |
1.00 |
1.00 |
0.69 |
-0.01 |
0.61 |
-0.71 |
0.62 |
-0.12 |
-0.39 |
|
||||||||
|
TSS |
-0.35 |
0.78 |
0.80 |
0.13 |
0.57 |
0.97 |
-0.20 |
0.95 |
0.48 |
0.18 |
0.80 |
|
|||||||
|
COD |
-0.51 |
0.64 |
0.66 |
0.04 |
0.57 |
0.94 |
-0.13 |
0.99 |
0.50 |
0.19 |
0.66 |
0.95 |
|
||||||
|
TP |
-0.85 |
0.04 |
0.07 |
-0.68 |
1.00 |
0.81 |
0.64 |
0.71 |
0.98 |
0.87 |
0.07 |
0.63 |
0.63 |
|
|||||
|
Hardness |
0.34 |
-0.22 |
-0.24 |
0.40 |
-0.76 |
-0.61 |
-0.44 |
-0.40 |
-0.73 |
-0.69 |
-0.23 |
-0.52 |
-0.31 |
-0.76 |
|
||||
|
Al |
-0.77 |
-0.58 |
-0.55 |
-0.97 |
0.81 |
0.24 |
0.98 |
0.11 |
0.87 |
0.98 |
-0.56 |
-0.02 |
0.00 |
0.76 |
-0.60 |
|
|||
|
Mg |
0.93 |
0.27 |
0.25 |
0.63 |
-0.65 |
-0.48 |
-0.53 |
-0.58 |
-0.68 |
-0.58 |
0.25 |
-0.31 |
-0.56 |
-0.65 |
0.01 |
-0.52 |
|
||
|
Ca |
0.93 |
0.63 |
0.61 |
0.95 |
-0.74 |
-0.24 |
-0.90 |
-0.23 |
-0.80 |
-0.86 |
0.61 |
0.01 |
-0.17 |
-0.69 |
0.23 |
-0.87 |
0.85 |
|
|
|
Cu |
-0.59 |
-0.81 |
-0.79 |
-0.95 |
0.56 |
-0.12 |
0.98 |
-0.24 |
0.64 |
0.85 |
-0.80 |
-0.37 |
-0.33 |
0.49 |
-0.38 |
0.94 |
-0.38 |
-0.81 |
|
|
Fe |
-0.57 |
0.61 |
0.63 |
-0.09 |
0.72 |
0.99 |
0.01 |
0.99 |
0.64 |
0.37 |
0.62 |
0.97 |
0.97 |
0.77 |
-0.53 |
0.18 |
-0.52 |
-0.23 |
-0.18 |
The pH predominated in class II, point 6 was presented in class IV and the others in class I. This may be related to the physical characteristics of point 3. This parameter showed a tendency of positive correlation with EC and TDS, TSS, total Fe and Fe (Table 8), which was a correlation already observed in some parameters in previous seasons.
The DO at points 1, 3 and 4 was presented in class I and point 5 in the Special class. It was observed that there was a decrease in the concentration of this parameter in relation to previous seasons. This may be related to the increase in TSS concentration, in the surface water temperature absorbed by solids (Kannel et al., 2007; Naveedullah et al., 2016), by OM, COD and Fe, resulting in DO consumption so much so that the correlation matrix (Table 8) showed that the DO has a negative correlation tendency with the TSS, the OM, Fe and COD.
The EC remained in the Special class, as in previous seasons, and also showed a very high positive correlation trend for pH and TDS, and high for ALK, Fe, Cl, TSS and Ca parameters. This can be explained by the entry of materials into the water body due to the increase in precipitation and, consequently, the release of Fe and Ca ions, which tended to show a positive correlation.
The Cl- was another parameter that remained in the same class as autumn and was not detected at points 4 and 5. SO4-2, as well as Cl-, which remained in class I (<250), compared to the previous season.
TP specifically presented only in point 1 with class II (<0.030) and in point 3 with classification in class III (<0.05). This also showed a high trend of positive correlation with OM, which may be associated with anthropic or natural actions (Von Sperling and Chernicharo, 2002; Oliveira et al., 2017). The concentration of this element may also be associated with the accumulation of organic matter from plant material (Andrade et al., 2008), dissociation of suspended sediments and soil leaching by precipitation (Marins et al., 2007; Santos et al., 2010).
The Fe presented a general classification in class III (<5.0), with a trend of positive correlation between the color, turbidity, COD, TDS and TSS parameters, which already presented this correlation characteristic in previous seasons.
The Al remained in class IV (>0.2), having increased its concentration: in point 4, which doubled the concentration and in point 3 where it was tripled. On the other hand, points 4 and 5 showed a drop in concentration, and were identified in class III showing once more a trend of positive correlation with SO4-2, OM, color, turbidity and TP.
Monitoring of surface water quality in the summer of 2020
This season is characterized by an increase in precipitation, with an average value of 200 mm, as well as in the volume of plant density and temperature, providing the acceleration of litter decomposition processes. The general classification registered in the summer was in class III (Table 9), quite different from what was observed in the other seasons.
Table 9 - Analytical results of the physical and chemical parameters of surface water and their classification according to UW Resolution 357/2005 CONAMA in the summer.
|
Parameters analyzed |
Unit of measure |
Monitoring points |
Classification by parameter |
|||||
|
4 |
5 |
6 |
7 |
8 |
||||
|
Physical Parameters |
Temperature |
°C |
19.6 |
20 |
19.8 |
19.8 |
19.7 |
|
|
Color |
Pt/Co |
712 |
332 |
412 |
178 |
284 |
II |
|
|
Turbidity |
NTU |
35.4 |
19.1 |
27.3 |
12.4 |
25.4 |
I |
|
|
TDS |
mg/L |
19.75 |
24.7 |
12.65 |
10.75 |
11.15 |
I |
|
|
TSS |
mg/L |
45 |
16 |
0 |
0 |
1 |
|
|
|
Chemical Parameters
|
pH |
|
5.85 |
6.29 |
5.86 |
6.14 |
6.3 |
II |
|
DO |
mg/L |
10.26 |
10.3 |
10.58 |
9.97 |
9.93 |
I |
|
|
EC |
µS/cm |
39.5 |
49.4 |
25.3 |
21.5 |
22.3 |
S |
|
|
AKL |
Ppm |
20.7 |
26.5 |
11.2 |
10.4 |
12.1 |
|
|
|
OM |
Ppm |
6.08 |
8.56 |
6.82 |
6.8 |
5.28 |
|
|
|
Cl- |
Ppm |
0.321 |
0.239 |
0.218 |
0 |
0.089 |
I |
|
|
SO4-2 |
Ppm |
0.278 |
0.124 |
0.123 |
0.18 |
0.08 |
I |
|
|
Total Fe |
Ppm |
5.278 |
5.774 |
11.655 |
2.905 |
5.122 |
|
|
|
COD |
mg/L |
25.47 |
35.88 |
45.96 |
216.65 |
26.59 |
|
|
|
TP |
Ppm |
0 |
0.004 |
0.003 |
0 |
0.001 |
I |
|
|
Hardness |
Ppm |
5 |
18 |
0 |
12 |
17 |
|
|
|
Al |
Ppm |
0.18 |
0 |
0.18 |
0.04 |
0.47 |
II |
|
|
Mn |
Ppm |
0 |
0 |
0 |
0 |
0 |
- |
|
|
Mg |
Ppm |
1.53 |
2.43 |
0.68 |
0.95 |
0.93 |
|
|
|
Na |
Ppm |
1 |
1 |
1 |
1 |
1 |
|
|
|
Ca |
Ppm |
1.48 |
2.72 |
1.51 |
1.74 |
1.61 |
|
|
|
Cu |
Ppm |
0.04 |
0.05 |
0.05 |
0.05 |
0.06 |
|
|
|
Fe |
Ppm |
20.6 |
8.19 |
0.05 |
2.75 |
4.68 |
III |
|
|
Average rating per point |
II |
I |
I |
I |
I |
Overall Summer Ranking I |
||
Turbidity concentrations in the summer at points 2 and 4 were in the Special class and points 1, 3 and 5 in class I. In general, with class I, there was a trend of positive correlation with TSS and Fe (Table 10). It is important to note that in this season, there were changes in land use and land cover, namely the forestry harvest near points 1 and 3, which did not show major changes such as increased concentration of TSS and OM, and may increase its concentration in the rainy season, as already observed in other works (Anjinho et al., 2020). It can be observed that changes in land use and land cover, such as harvesting activity, are related to the increase in the concentration of solids in the water (Ensign and Mallin, 2001; Lima and Zakia, 2006).
The TDS was presented in all points in class I due to the higher vegetation density in the summer. It also showed a positive correlation trend with EC, Cl-, ALK, Fe and Ca (Table 10). The highest TDS values were detected in areas containing pasture, such as points 1 and 2, places where increased TDS concentration can unbalance biochemical reactions (Wang et al., 2013).
The color showed varied concentrations at the collection points, and the highest one was in the summer. This is due to the increase in precipitation, which caused an increase in dissolved substances and organic materials, with a record of a positive correlation trend between SO4-2, TSS, Fe and Turbidity (Table 10).
Table 10 - Correlation matrix of physical and chemical parameters of surface water at UW in the summer of 2020.
The pH showed a slightly elevated acidity level, in class IV for points 1 and 3 (Table 9). This is due to the influence of increased precipitation (Damasceno et al., 2015; Alvarenga et al., 2016; Ben-Eledo et al., 2017). The pH showed a tendency of negative correlation with the OM, in such a way that when the decomposition processes accelerate, there is an increase in the pH (Table 10). Points 2, 4 and 5 were classified within the limit established for class I (Table 9). It should be noted that although points 1 and 3 have little in common in terms of land use and land cover, both showed the most altered parameters among all the analyzed stations, especially point 3.
The DO at points 1, 2 and 3 with the Special class and points 4 and 5 with class I (Table 9) indicated that there was greater water oxygenation at this station, and that may be related to the increase in water volume of precipitation (Zanata et al., 2015; Araújo et al., 2018).
The EC featured in the Special class at all points for this season. This parameter reached the highest values in this season, showing a trend of positive correlation with TDS, TSS, as well as ALK, EC, Mg and Cl- (Table 10).
The ALK showed the highest values in this season, focusing on points 1 and 2. It had a tendency towards a positive correlation with TDS, TSS, TP, Cl-, Ca and Mg (Table 10) due to increased precipitation and ion availability originating from the dissolution of rock and soils (Lima and Zakia, 2006; Cruz et al., 2016).
The Cl- and SO4-2 parameters at all points showed class I values. The sulfates detected in natural water resources are also ions that can come from the dissolution of soils and rocks (Fernandes et al., 2012). Cl- tended to have a positive correlation with turbidity, TDS and TSS, and SO4-2with color, TSS and Fe.
The TP was in the special class at points 1 and 4 and at points 2, 3 and 5 in class I. The TP showed a trend of positive correlation with the OM (Table 10). Not only P, but nitrogen and K are nutrients that may be related to the increase in water discharge, runoff, over the forest exploitation area (Malmer and Grip, 1994; Vital et al., 1999). Al resulted in the Special class (0) at point 2; class I (<0.1) at point 4; class II (>0.1) at points 1 and 3, and class III (<0.2) at point 5. In general, for summer, with class II, it showed a very high trend of positive correlation with OM, ALK, EC and DO.
Total Fe (dissolved more in suspension) showed high concentrations in this season and Fe showed higher concentrations at points 1, 2, 4 and 5, overall with class III (<5.0). This is due to natural conditions with the geological (Ipt, 1981) and pedological formation of the basin as there is an occurrence of iron concentration in the other stations.
Correlation Analysis
Water quality presents variations in the concentration of its elements depending on several factors, including land use and cover and time (Rodrigues et al., 2018). In all stations, through the correlation matrices (Tables 4, 6, 8 and 10), for example, the parameters TDS and EC presented interaction with R2 above 0.98 (Fig. 6). They were the only ones in all stations to maintain the occurrence of high R2, as they are associated with the occurrence of several parameters such as Ca, Mg, ALK, pH and Hardness. The influence of seasonality between parameters stands out, for example, between parameters such as DO x pH and TDS x ALK, as the volume of precipitation increases (Fig. 3) and the DO concentration increases due to the greater turbidity of the water, consequently increasing the concentration of dissolved salts, TDS and pH so much so that Mendes and Ferreira (2014) detected a negative correlation between precipitation and pH and attributed this result to the leaching of Cerrado soils.
The ALK and TDS parameters, in autumn, resulted in an R2 of 0.97 due to the association with EC and Ca, Mg, Cl- and Cu ions, already presented in the trend matrices. It can be inferred that this pair of parameters is correlated with seasonality, in addition to TDS and EC, as the measure that decreases the entry of water into the system, precipitation, the correlation trend is practically null.
The SO4-2 and color, in spring, resulted in R2 of 0.99, both associated with Turbidity and TDS. The pH and TDS, in spring, also resulted in R2 of 0.99, associated with Ca and Mg cations, which in turn are associated with EC and ALK parameters. It can also be observed that these associations occur between the organoleptic parameters of water. TDS and Mg also resulted in an R2 0.89, which was related to EC, ALK and TDS.
Fig. 7 presents the concentration of some of the parameters for the entire period that can be highlighted in the analysis, as demonstrated by Alvareda et al. (2020).
The quartile sampling of Fig. 7 showed that points 1, 2 and 3 were the ones that stood out due to the occurrence of concentrations between the evaluated parameters, which may be related to erosive features and anthropic interventions such as cattle raising, pasture reform and areas of silviculture harvested, given that the latter alters the concentrations of nutrients and sediments in water bodies (Rodrigues et al., 2019).
Points 1, 2 and 3 are located on pasture areas mostly intended for cattle raising, which may be influencing the increase in parameter concentrations, as the highest concentrations of TP are in these areas, as this parameter is closely associated with the analysis of the impacts of agricultural activities (Assis and Azevedo Lopes, 2017).
Another aggravating factor is the suppression of riparian vegetation, which is important in containing/barring the entry of organic and inorganic elements into the water body (Connolly et al., 2015), as riparian zones have long been known to help stabilize biodiversity and water quality (Souza et al., 2013; Fernandes et al., 2014; Keir et al., 2015).
In point 3, in addition to the influence of agriculture and livestock, there is also the action of forestry harvesting activities and the erosive processes installed in the river channel (Fig. 8) that may influence the entry of elements/materials that corroborate with the increase in the pH and concentration of turbidity and Al.
The occurrence of the concentration of some parameters not only in point 3 but also in points 1 and 2 has been observed since the first collection, such as turbidity, TDS, Al, pH and Fe. These concentrations are influenced by the chemical characteristics of the soil, with Fe (Ipt, 1981) and Al is abundant in the soils of the Brazilian cerrado.
Points 4 and 5 were the ones that presented the concentrations of the parameters with the best classifications, which may be related to the classes of land use and land cover, as these together presented the largest area of planted forests and natural vegetation. In addition, water quality may be related to several factors such as seasonality, configuration of land use and land cover, spatial and temporal scale and other intrinsic characteristics (Xiao et al., 2016).
The methodology adopted in monitoring water quality helped in the seasonal analysis of the watershed as seasonality showed it was related to changes in water quality, mainly derived from rainfall, geological/soil and use, cover and management changes of the land, which are partly reflected in the limits established by the legislation and in the specificities of the domain of the Brazilian cerrados. There was more emphasis on iron concentrations, derived from the Santo Anastácio sandstones, in a greater proportion in the Vale do Rio do Peixe, and aluminum in the soils.
Interactions between water quality parameters also followed conventional associations such as ALK and EC, TDS and EC, Color and Turbidity, TSS and Turbidity, EC and Ca, EC and Mg. Moreover, ALK and TDS were the main parameters in which they were detected with the influence of seasonality.
Having obtained the results, we suggest adopting measures such as: the recomposition and recovery of the riparian vegetation in the spring areas, especially in point 2; recovery of areas affected by erosion processes installed in the basin; the implementation of soil conservation techniques on the properties and on neighboring roads and the improvement in land management, both in terms of soil and animals, respecting seasonality and slopes, and focusing on livestock; constant monitoring of surface water quality and fencing and maintenance of riparian forests.
Seasonal monitoring of water quality, which should be required by law in case of intensified land use, may serve as a support tool for future studies of analysis of environmental fragility and soil loss, contributing to the planning and management of watersheds.
Acknowledgment
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 and in part by the Fundação Universidade Federal de Mato Grosso do Sul – UFMS/MEC – Brazil. We would like to thank the Conselho Nacional de Desenvolvimento Científico and Tecnológico (CNPq) for funding the research project (process nº 403993/2021-0) and research productivity scholarship awarded to the last author (process nº 306448/2020-3). We would also like to thank the financial support of the PROPP/UFMS research project, nº 82/2018 and the Graduate Program in Geography at the Três Lagoas Campus of UFMS for the payment of the English language editing service.
Data availability statements
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Funding
This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Financing Code 001 and the doctoral scholarship granted to the first author; the Federal University of Mato Grosso do Sul Foundation – UFMS/MEC – Brazil; the Conselho Nacional de Desenvolvimento Científico and Tecnológico (CNPq) for the research project (process nº 403993/2021-0) and research productivity scholarship granted to the last author (process nº 306448/2020-3); the PROPP/UFMS research project, nº 82/2018; and by Graduate Program in Geography at the Três Lagoas Campus of UFMS.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Author Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Víncler Fernandes Ribeiro de Oliveira]. The first draft of the manuscript was written by [Víncler Fernandes Ribeiro de Oliveira] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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No competing interests reported.