Next Article in Journal
Beyond Biomass: Reimagining Microalgae as Living Environmental Nano-Factories
Previous Article in Journal
Seasonality of Pharmaceuticals and Personal Care Products in Shallow Lakes, Florida, USA—Part A
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Environments
Volume 12
Issue 7
10.3390/environments12070220
environments-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of Permanent Preservation Areas on Water Quality in a Semi-Arid Watershed

by
Fernanda Helena Oliveira da Silva
1,*,
Fernando Bezerra Lopes
1,*,
Bruno Gabriel Monteiro da Costa Bezerra
1,
Noely Silva Viana
1,
Isabel Cristina da Silva Araújo
1,
Nayara Rochelli de Sousa Luna
2,
Michele Cunha Pontes
3,
Raí Rebouças Cavalcante
4,
Francisco Thiago de Alburquerque Aragão
5 and
Eunice Maia de Andrade
1
1
Department of Agricultural Engineering, Campus Pici, Federal University of Ceará, Fortaleza 60440-554, Brazil
2
Municipal Secretariat of Education of Fortaleza, Fortaleza 60170-173, Brazil
3
Department of Ecology and Natural Resources, Federal University of Ceará, Campus Pici, Fortaleza 60440-900, Brazil
4
Technical Assistance and Rural Extension Agency of Ceará, Tauá 63660-000, Brazil
5
Ieducare University Center (FIED), Tianguá 62320-121, Brazil
*
Authors to whom correspondence should be addressed.
Environments 2025, 12(7), 220; https://doi.org/10.3390/environments12070220
Submission received: 25 April 2025 / Revised: 17 June 2025 / Accepted: 19 June 2025 / Published: 27 June 2025
(This article belongs to the Special Issue Rural Landscape Management and Conservation: Advances in GIS and Remote Sensing Applications)
Browse Figures
Versions Notes

Abstract

Water is scarce in semi-arid regions due to environmental limitations; this situation is aggravated by changes in land use and land cover (LULC). In this respect, the basic ecological functions of Permanent Preservation Areas (PPAs) help to maintain water resources. The aim of this study was to evaluate the relationship between the LULC and water quality in PPAs in a semi-arid watershed, from 2009 to 2016. The following limnological data were analyzed: chlorophyll-a, transparency, total nitrogen and total phosphorus. The changes in LULC were obtained by classifying images from Landsat 5, 7 and 8 into three types: Open Dry Tropical Forest (ODTF), Dense Dry Tropical Forest (DDTF) and Exposed Soil (ES). Spearman correlation and principal component analysis were applied to evaluate the relationships between the parameters. There was a significant positive correlation between DDTF and the best limnological conditions. However, ES showed a significant negative relationship with transparency and a positive relationship with chlorophyll-a, indicating a greater input of sediments and nutrients into the water. The PCA corroborated the results of the correlation. It is therefore essential to prioritize the preservation and restoration of the vegetation in these sensitive areas to ensure the sustainability of water resources. Future studies should assess the impact of specific human activities, such as agriculture, deforestation and livestock farming, on water quality in the PPAs.

1. Introduction

Water is a dynamic natural resource that is essential for life; however, its availability has been compromised in several regions of the world [1]. Water scarcity is intensifying, not only because of the growing demand for goods and services, but also due to the quality of the water being compromised through environmental degradation [2]. Sarzaeim et al. [1] point out that water management faces greater challenges in the current scenario of climate change, particularly due to the increased demand for water. In addition to population growth, this situation is aggravated by other factors, such as the discharge of domestic effluent, indiscriminate deforestation, and the intensive use of agricultural inputs. The authors of [3] demonstrated a direct relationship between such usage and the eutrophication of aquatic ecosystems.
In this respect, watersheds play a crucial role as management units. Mahessar et al. [4] underlined this by documenting the consumption of polluted water in areas of a watershed in Pakistan. Noriega et al. [5] also emphasize the strategic role of watersheds as basic units for water planning, where their management is directly influenced by the type of land use and land cover (LULC).
In semi-arid regions, the problem of water availability becomes even more critical due to the high spatial and temporal variability of rainfall [6]. This variability was demonstrated by Sanguesa et al. [7] who analyzed rainfall concentration over time and space using different indices. Similarly, studies such as those by [8], Andrade et al. [9] and Souza et al. [10] corroborate this scenario, highlighting the instability of rainfall patterns in the semi-arid region of Brazil. Furthermore, the high rates of evapotranspiration, quantified by Souza et al. [11] and Knipper et al. [12] using remote sensing, make a significant contribution to the loss of surface water in these regions.
Maia, Lopes and Andrade [13] showed that LULC varies by season in a watershed of the semi-arid region, affecting water availability in the reservoir over time. Lopes et al. [14] conducted a detailed assessment of water quality in the same region, highlighting the negative impact of human occupation on the watersheds. More recently, Praxedes et al. [15] used mass balance to highlight the increasing accumulation of nutrients (nitrogen and phosphorus) in semi-arid reservoirs, indicating a high risk of eutrophication.
Given the importance of water, and the challenges of semi-arid regions, water resource management becomes essential. In this respect, water quality is seen as a central factor, especially in reservoirs located in arid and semi-arid regions [16]. Chou et al. [17] emphasize that reservoir management is a critical issue due to its significant impact on the natural environment and human life. Continuous monitoring of the ecological health of water in artificial environments is therefore urgent [18]. Several studies have shown that urbanization, agriculture and deforestation have a significant impact on water quality in bodies of water [19,20]. Chaves et al. [21] and Duarte et al. [22] point out that agricultural and urban activities introduce nutrients and pollutants into aquatic systems, altering their physicochemical and biological characteristics. The authors of [21] confirm these effects, analyzing different land-use patterns and their repercussions on water quality, while Praxedes et al. [15] quantified the effects using environmental modeling. Lima et al. [23] estimated phosphorus decay coefficients based on hydrological models, allowing a more accurate diagnosis of water quality.
The vegetation cover of Permanent Preservation Areas (PPAs) acts as a protective barrier, reducing the input of sediments and nutrients, as shown by [24]. For this reason, PPAs are crucial for helping to manage and protect water. These PPAs are legally protected zones established around water bodies such as reservoirs and in other environmentally vulnerable areas. The main goal of the PPAs is native vegetation conservation. In the case of reservoirs, PPAs are typically delineated based on the maximum water level, remaining fixed regardless of seasonal fluctuations of the water level in the reservoir.
Cocco et al. [25] underline this importance, showing that preserving riparian vegetation classified as a PPA reduces the entry of harmful substances into aquatic environments, while the authors of [26] link the degradation of these areas to increased siltation and eutrophication. However, the criteria for delimiting PPAs, as defined by the new Forest Code (Law No. 12.651/12), may not be suitable for all ecosystems, failing to consider their specific needs [27]. Therefore, a thorough understanding of the role of PPAs in watershed management is essential.
To monitor and manage the complexity of these systems, remote sensing has emerged as a valuable tool. Given these challenging scenarios, geotechnology is now indispensable for the planning and sustainable management of water resources [13,28]. Such technology makes it possible to map, monitor and model the impact of human action and climate variability on hydrological dynamics and water quality [29,30]. Furthermore, it facilitates the identification, delimitation and analysis of LULC in PPAs [31], providing valuable information to support technical decisions regarding the implementation of conservation measures [32] and restoration [33] in the management of water resources.
Given this scenario, the aim of this study was to analyze how changes in LULC in the PPAs of a watershed influence water quality in a semi-arid reservoir, considering both physicochemical and biological parameters over an eight-year period. Since the new Forest Code allows the size of riparian forests to vary between 30 and 100 m, the study also assessed which size shows the best correlation between LULC and water quality.

2. Study Area and Methods

2.1. Study Area

The study area comprises the watershed of the General Sampaio Reservoir (WGSR), which is part of the Curu river network. The basin drains an area of 1720 km2 in the north of the state of Ceará, in the district of General Sampaio, located at 4°3′10″ S and 39°27′16″ W (Figure 1).
According to the Köppen classification, the climate in the region is type BSh′w′, hot semi-arid, with an average monthly temperature always above 18 °C and rainfall predominantly in the autumn. A study of the monthly rainfall in Ceará shows that the rainy season occurs mainly between January and April (accounting for 70% of the annual rainfall), with a transition period from May to June (with small-scale rainfall events), and a dry season from July to December [5,34,35].
In this study, the rainy season was extended to include the transition period, when the rainfall is less intense. This approach is particularly important when assessing variations in land use and occupation, since the Caatinga biome, which is unique to the semi-arid region, responds differently to changes in rainfall and to drought conditions [36]. This biome has a relatively stable response to the seasonality of the rainfall, with a longer growing season that is less affected by variations in the length of the rainy period [37,38]. This stability is attributed to the ability of the ecosystem to soften the effects of interannual rainfall variability through such mechanisms as soil moisture retention and the dynamic adaptation of plant growth [36,39].

2.2. Datasets

The study was developed in three main stages: image processing, delimiting the WGSR, and the collection and analysis of the limnological data. Figure 2 shows the essential steps of the general methodology of each process.

2.2.1. Water Quality

The limnological variables included the transparency (m), total nitrogen (mg L−1), total phosphorus (mg L−1) and chlorophyll-a (mg L−1) from 2009 to 2016. Water samples were collected from four points: P01—water intake used for public water supply; P02—near the fish farm; P03—near the entrance to the Salvação stream; and P04—near the entrance to the Curu river (shown in Figure 1), giving a total of 108 analyses. Each of the water samples was collected at a depth of 30 cm from the surface.
From 2014 to 2016, water samples and data were collected by a team from the Research and Extension Group on Water and Soil Management in the Semi-Arid Region (MASSA/UFC) from each of the four sampling points (Figure 1). Transparency was measured in situ using a Secchi disk. The total nitrogen, total phosphorus and chlorophyll-a analyses were carried out at the Environmental Chemistry Laboratory (LAQA/UFC) as per [40]. Data for the years 2009 to 2013 were obtained from the hydrological portal of the Water Resources Management Company (COGERH), which collected samples at point P01. The COGERH samples were collected 30 cm from the surface and stored in dark bottles to avoid exposure to light. These were later analyzed by accredited laboratories following a standardized protocol. Chlorophyll-a was determined using the APHA 10200 H spectrometric method, total phosphorus as per SMWW 24th Edition, Method 4500 P, and nitrogen using SMWW 24th Edition, Method 4500 NH3–F.
Rainfall data were made available by the Ceará Foundation for Meteorology and Water Resources (FUNCEME). The volume of the reservoir was obtained from the COGERH website (Figure 3).
Images were taken from the Landsat 5 and 7 satellites using TM and ETM sensors, respectively, and from the OLI sensor onboard Landsat 8, all with reference to orbit 218, point 63 (Table 1). The images were obtained from the United States Geological Survey (USGS) database as well as the 1 arc-second Global Digital Elevation Model (DEM) version 3.0.

2.2.2. Land Use and Land Cover

Once downloaded, the images were adjusted to the UTM coordinate system, WGS 84, Zone 24S. The DEM images were used to map the boundaries of the basin using the ArcHydro extension of ArcGIS 10.1 software. When determining the watercourses, the cells corresponding to the dam of the General Sampaio Reservoir were defined as the endpoint of the water flow.
Using the radiometric calibration procedure in the ENVI 5.1 software, all of the bands were then converted from digital numbers (DNs) to radiance measurements at the top of the atmosphere as per Equation (1).
L = G a i n ∗ D N   p i x e l + o f f s e t ,
where L: radiance (watts/m2); DN pixel: digital number of the pixel; offset: detector alignment (watts/m2).
For the atmospheric correction, the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH), version integrated into ENVI 5.0. algorithm was applied, based on the Moderate Resolution Atmospheric Transmission Model (MODTRAN 4.0). Selecting the tropical atmospheric model, the following descriptive characteristics of the atmosphere were determined: scale (single scale factor) equal to 10, aerosol scale height equal to 1.08 km, and CO2 concentration equal to 400 ppm. The values used for the other parameters were predefined by FLAASH; since no stations allow these parameters to be determined for the date of the satellite pass, the default values of the software were used [41,42].
The maximum likelihood method (MAXVER) of ArcGIS 10.1 was used to classify land use and ground cover. This method consists of using pure pixel sampling for each class based on patterns of color, texture and shape, called regions of interest, and then using these samples to classify each of the areas [43]. The following classes were determined: Water, Cloud, Cloud Shadow, Exposed Soil (non-vegetated areas), Open Dry Tropical Forest (DTF) and Dense Dry Tropical Forest (DTF). The classification was validated using a confusion matrix.
The delineation of the PPAs was based on the guidelines established by Federal Law No. 12.651 of 17 October 2012. The methodological approach is summarized in the flowchart shown in Figure 4. Article 5 of Federal Law No. 12.651 states the following: “When implementing an artificial water reservoir for the generation of energy or public supply, the entrepreneur must acquire, expropriate or establish an administrative easement for the Permanent Preservation Areas created around it, as established by environmental licensing, observing a minimum width of 30 (thirty) m and maximum of 100 (one hundred) m in rural areas, and a minimum width of 15 (fifteen) m and maximum of 30 (thirty) m in urban areas (Extracted from Law No. 12.727, of 2012)”.
In this study, buffer zones of 30 and 100 m were delineated around the reservoir to assess the sensitivity of the legal limits defined for PPAs surrounding reservoirs used for public water supply. This approach aimed to evaluate how land use and occupation within these legally established ranges may influence water quality and ecological dynamics in the riparian environment.
To delimit the riparian forest in the reservoir, topographic maps were obtained from the Brazilian Army Geoportal website. The entire delimitation procedure was carried out using ArcGIS software. The marginal strips of any perennial or intermittent natural watercourse were identified, excluding ephemeral watercourses with a width of less than 10 m (Table 2).
To determine PPAs that were on a slope, SRTM DEM was used to generate a slope map of the basin in degrees using the Slope function in ArcGIS; all the areas with a slope greater than 45° were highlighted.
It was assumed that the springs are not only upstream of the starting point of each watercourse formed in the basin, but also in a higher area. The springs were therefore identified based on the drainage network using the Feature Vertice To Point function. Maps were also produced defining the 50-m radius buffer around each spring.
To delimit the PPAs on hills and mountains, the slope map of the area and the DEM of the basin were used to carry out the 36 procedures for delimiting hills and mountains as prescribed in law [44] (Table 3).

2.3. Statistical Analysis

Once the PPAs had been delimited, a clip was made between the image classification and the PPA shapefile, thereby obtaining the land use and occupation data for these areas. The analysis of variance was determined for the areas of each PPA land-use class. The SPSS 29 and Excel software were used to carry out the statistical measurements.
The Spearman method is a non-parametric method used when the relationship between two variables is not linear (monotonic correlation). Spearman’s correlation was therefore determined between the areas of the PPA land-use classes and the limnological variables, as per Equation (2). The correlation coefficients were tested for significance at 5%.
r s = ( 6 ∑ D 2 ) n   n 2 − 1 ,
where rs—correlation estimator; n—number of data points for both variables; D—difference in the range of element n.
To determine how the water quality parameters contributed to discriminating between land uses, principal component analysis (PCA) was carried out using SPSS software.

3. Results and Discussion

3.1. Descriptive Analysis of the Water Quality

Typical water quality patterns in the General Sampaio Reservoir are presented in Figure 5 and Figure 6. Figure 5 illustrates the temporal trends of key limnological parameters, while Figure 6 summarizes their distribution and variability, highlighting transparency (A), total phosphorus (B), chlorophyll-a (C) and total nitrogen (D).
Water transparency has become more variable over the years, with higher values generally during dry periods, especially between 2009 and 2011 (Figure 5). This may be due to lower turbidity and reduced sediment input during periods of low rainfall.
Nitrogen levels have increased progressively over the years, showing more-marked peaks since 2013, which may indicate an accumulation or increase in the supply of nutrients to the ecosystem, favoring eutrophication. Phosphorus remained at relatively low concentrations throughout the period under analysis, with little significant variation. Even at reduced concentrations, phosphorus can act as a limiting nutrient, playing a crucial role in primary productivity.
Chlorophyll-a, associated with phytoplankton biomass, showed notable peaks in 2014 and 2015, especially during dry periods, suggesting that algae production was high at these times. There was a possible correlation between the increase in nitrogen and chlorophyll-a, meaning that increased nutrient availability may be responsible for the growth of phytoplankton. In addition, there is a tendency for transparency to decrease whenever there is an increase in chlorophyll-a, which suggests a higher concentration of algae in the water column with a consequent deterioration in water quality. In general, the data indicate a gradual process of eutrophication in the reservoir over the years, with emphasis on the increase in nitrogen and algal biomass.
The mean annual transparency of the reservoir was 1.77 m for the dry season and 0.97 m for the rainy season (Figure 6A), which may be associated with the increase in rainfall during the first six months of the year in the region, as shown in Figure 3. Variations in the transparency of the water in the reservoir throughout the year reflect the climate and hydrological conditions of the semi-arid region [21], where the dry season is characterized by a reduction in rainfall events [9,10] and greater evaporation [12]. During the dry season, therefore, the amount of sediment in the water is lower, and the reservoir is more transparent.
The transparency of the water shows significant seasonal variation due to the influence of various hydrological and meteorological factors. During the dry season, the transparency increases, as there is a reduction in the amount of sediment from surface runoff, which is generally more intense during heavy rainfall.
On the other hand, during the rainy season, transparency tends to decrease due to the transport of sediments caused by rainfall events. The authors of [29] assessed the water quality of the General Sampaio Reservoir between 2017 and 2018 and found that recent rainfall caused sediment to be carried into the reservoir, reducing its transparency. Sediment resuspension also occurs, increasing the turbidity of the water. These processes directly affect water quality and can impact the structure and functioning of aquatic ecosystems [22,45].
In the period of study, the TN mean values ranged from 1.11 mg L−1 to 2.48 mg L−1 (Figure 6D), while the average TP mean values ranged from 0.12 mg L−1 to 0.62 mg L−1 (Figure 6B). These variations can be attributed to various factors, such as runoff from agricultural fertilizers, leaching of the soil and contributions from domestic and industrial sewage during the rainy season, all of which increase the nutrient load in bodies of water [5,15,18,46].
The average values for Chl-a ranged from 5.17 µg L−1 to 33.67 µg L−1. During the dry season, the average concentration in the reservoir was 23% higher compared to the rainy season (Figure 6C); this suggests the phytoplankton biomass was greater during the dry season. The Chl-a concentration is influenced by several environmental factors, including dissolved oxygen and nutrients, such as nitrates and phosphates [27,47]. Studies have shown that Chl-a concentrations tend to be higher during the dry season due to reduced water flow, which limits nutrient dilution and promotes the growth of phytoplankton [47,48,49].
In 2009, when the reservoir reached its maximum capacity, Chl-a concentrations were 17.8 µg L−1 during the rainy season and 4.72 µg L−1 during the dry season, relatively low values compared to other years. This suggests an inverse relationship between water volume and Chl-a concentration, where lower volumes, such as during the dry season of 2016, with values of up to 51,24 µg L−1, favor higher concentrations due to less nutrient dilution. According to [48], increased water storage can reduce Chl-a levels during the rainy season, while during the dry season, reduced water input and more-eutrophic conditions can result in higher concentrations.
During the dry season, the values for Chl-a, TN and TP were higher than during the rainy season. Of note is TP, which was higher during the eight dry seasons under study than during the rainy seasons. The values for turbidity, apparent color and total solids were always higher during the rainy season, while the opposite was found for transparency. These physical parameters reflect the influence of surface runoff, particularly in areas with high erosion potential and significant sediment yield, as reported by previous studies [23,28,50].
During the dry season, there is a reduction in the volume of water stored in the reservoir, which contributes to a higher concentration of sediments and pollutants in the water, and which resulted in a significant variation in the mean values of the limnological variables during the wet and dry seasons, with a 60% and 13% increase in TP and TN concentration, respectively, during the dry season.
Nutrient concentrations were highly dispersed. TP showed a standard deviation (SD) of 0.21, which is higher than the mean value of the data (0.20) (Figure 6B). For TN, the SD was 0.64. However, TN had the lowest coefficient of variation among the variables under evaluation. This is a consequence of the large number of factors that can affect the concentration of these nutrients, such as poorly managed agricultural activity, livestock farming, deforestation, the dumping of waste and the weather conditions, particularly rainfall, wind and temperature [14,23,26,51].
Zhang et al. [52], comparing the impact of river flow on surface water quality in the Xiangjiang River in China based on 12 water quality parameters over 10 years, also found that the TN and TP concentrations were significantly higher during the driest periods in the region.
To assess the quality of the reservoir water, the National Environment Council (CONAMA) developed guidelines that divided bodies of water into different classes. Class 2, which the reservoir fell into for 12 of the 16 periods under evaluation, requires more extensive treatment than Class 1, but is still considered suitable for human consumption. The limits for this classification include up to 50 µg L−1 Chl-a, a minimum transparency of 0.5 m, a TN of up to 2.0 mg L−1 and a TP of up to 0.1 mg L−1.
Chl-a exceeded these values in 2015 and 2016, TN exceeded the limit in 2009 and 2011, while TP was never below the respective limit in any of the periods under study; unlike transparency, which was always greater than 0.5. During the first two and last two of the four periods, the reservoir was not classified as Class 2; it had a volume of 38% and 3.5%, respectively.

3.2. Use and Occupation of All Permanent Preservation Areas

The PPAs cover an area of 55.8 km2, corresponding to 3.5% of the total area of the WGSR at 1592.88 km2 (Figure 7). The riparian zones along the marginal strip of the watercourses carry out important environmental functions, such as controlling soil erosion and water quality and preventing the transport of sediment, nutrients and chemicals [53,54]. The PPAs around the rivers made up the highest percentage of the total area of the PPAs in the basin (79.01%).
The PPAs on the hills and mountains were almost entirely located in the upper third of the basin, where the highest elevations and, therefore, the steepest slopes are to be found (Figure 1 and Figure 7). The area occupied by the PPAs on the hills covers 3.5%, giving a total of 2.01 km2. The PPAs on slopes greater than 45° had a more-uniform distribution in the area of the basin, occupying 1.70 km2 of the total area of the PPAs.
The mean value for the 16 samples of land use and occupation in the protected areas of the river basin showed 35.6% of these areas to be composed of exposed soil. In addition, exposed soil was predominant in the lower third of the basin. Open DTF was the second most prominent class, and was found, on average, in 24.1% of the protected areas.
Figure 8 presents the temporal dynamics of LULC typologies, showing the variation in the extent of exposed soil, sparse vegetation and dense vegetation across rainy and dry seasons from 2009 to 2016. Figure 9 summarizes the statistical distribution of these classes within PPAs, emphasizing the seasonal differences in their spatial coverage.
There is a marked seasonal and inter-annual dynamic between the types of LUGC in the PPAs of the watershed (Figure 8), with emphasis on the significant increase in exposed soil over the years, particularly from 2013 onwards, with notable peaks during the dry periods of 2014 and 2015. The areas of sparse or dense vegetation are inversely proportional to the areas of exposed soil.
The dense vegetation shows a tendency to decrease over the years, with minimum levels coinciding with the peaks of exposed soil, suggesting degradation of the native vegetation cover. The sparse vegetation, on the other hand, fluctuates, albeit with a tendency to increase slightly in some years, possibly as a result of the partial degradation of the dense vegetation.
This dynamic of LULC in the PPAs appears to be directly related to the deterioration of water quality in the reservoir, as can be seen in Figure 4. The increase in areas of exposed soil may contribute significantly to the larger amount of sediment, nutrients and particulate matter transported to the reservoir, particularly during rainy periods. This contribution exacerbates such processes as reducing water transparency and increasing the nutrient and chlorophyll-a concentration, favoring eutrophication.
In general, the area of exposed soil decreased during the rainy season compared to the dry season, increasing by up to 44% between semesters, with 2013 being particularly noteworthy (Figure 9). As highlighted by [55], the period of drought between 2012 and 2018 in the state of Ceará had the longest average bivariate return period ever recorded, estimated at 240 years. This means that the severity and duration of the drought were exceptionally rare and unusual, as also shown in Figure 3 of this study.
At the same time, there was an increase in the areas of vegetation (both open and dense) during the first semester. These seasonal patterns reflect the typical climate characteristics in the region of the WGSR, since seasonal rainfall is the main factor that regulates plant phenology in the dry tropics [16]. Furthermore, these phenomena are also influenced by the interaction of various factors, such as human activity, including agriculture and deforestation.
In the second half of 2011 and 2012, around 37.5% and 43.6% of the PPAs were classified as cloud/cloud shadow. According to [56], clouds and their shadows reduce the useable area of the image and interfere with the quantitative analysis. Despite the limitations imposed by cloud cover, a classification accuracy of 89% was achieved.
The more pronounced presence of dense DTF during the rainy season reflects adaptation to the rainfall pattern characteristics of the region. Analysis of the rainfall indices reveals a significant increase during the rainy season, as shown in Figure 3. In the semi-arid region, the flora is dominated by deciduous species, which lose their leaves during the dry season; on the other hand, during the rainy season, plant activity shows vigorous growth [11].
Around 60 per cent of the APPs that should be protected are not being used for their intended purpose, in violation of the provisions of environmental legislation, compromising the balance of the watershed of the General Sampaio Reservoir.

3.3. Analysis of the Correlation Between Water Quality and Land Use in All PPAs

The Spearman correlation (Table 4) was calculated between the land-use classes open DTF, dense DTF and exposed soil and the four water quality parameters. Three significant values were found between these two groups. The first was the positive correlation between Chl-a and exposed soil. This showed that the larger the area of exposed soil in the areas that should be protected, the higher the concentration of Chl-a in the waters of the General Sampaio Reservoir. Negative values were obtained for open DTF (which is statistically significant) and dense DTF, indicating a possible reduction in Chl-a in these areas.
Chl-a is a key indicator of algal biomass and eutrophication [57] and is commonly used as a parameter of the eutrophic status of aquatic ecosystems. Nutrients such as nitrogen and phosphorus, which can flow into the reservoir via runoff from agricultural areas, and from domestic and industrial sewage, favor the growth of algae [58,59]. This is because the soil is susceptible to erosion and sediment loss without a layer of vegetation to protect it. According to [3], agricultural activity or soil degradation in the vicinity of bodies of water have a significant influence on the natural process of nitrogen and phosphorus accumulation over time. The authors of [60,61] also found that human activity, such as agriculture, which results in areas of exposed soil, has a significant effect on the physicochemical and biological parameters of the water.
Also of note is the significant negative relationship between exposed soil and transparency, where, as the amount of exposed soil increases, water transparency decreases. The authors of [62], evaluating the impact of rainfall on soil erosion and, consequently, on sediment deposition in reservoirs, found that the impact of annual rainfall can be seen on sediment load and on the correlation coefficient between the annual rainfall and sediment load, which was 0.94.
When the soil in the PPAs is exposed, erosion is more likely to occur, resulting in a larger amount of sediment being carried into the reservoir during rainfall events [62,63]. The suspended sediment in the water reduces clarity and transparency, making the water more turbid [58]. Therefore, the increased presence of exposed soil leads to a reduction in the transparency of the water in the reservoir.
The open DTF showed a significant correlation with Chl-a. However, in contrast to the exposed soil, the relationship was negative, suggesting that the more vegetation in the PPAs, the lower the concentration of Chl-a in the reservoir water, and the better the water quality. This is the result of a decrease in the erosion process, which reduces the export of particles to the bodies of water, with a consequent improvement in water transparency and reduction in the phosphorus load transported by the surface runoff.
In general, these results show that the greater the ground cover, the lower the surface runoff and the less sediment transported from the soil to the water [4,20]. In studies on the effects of land use on the qualitative aspects of water during the dry and rainy seasons, the authors of [64] identified correlations between the physicochemical parameters, confirming the results and their relationship with land use and occupation.

3.4. LCLU in Riparian Forests of the General Sampaio Reservoir

On the basis of these findings, it is clear that the recovery or maintenance of areas of riparian forest in PPAs plays a crucial role in decisions regarding the management of water resources in river basins. To visualize this spatially, Figure 8 presents a qualitative representation of the delimitation of the riparian zone and temporal changes in the water area of the General Sampaio Reservoir from 2009 to 2016. Based on this, the process of land use and occupation in the riparian forest of the General Sampaio Reservoir (Figure 10) and its correlation with the qualitative parameters of the water were analyzed.
During the study period, the volume of water stored in the reservoir underwent a reduction of 97% (Figure 3). The average level of the General Sampaio Reservoir reached 100% in early 2009, dropping to 2.5% by the end of 2016. The lowest total annual rainfall during the study period occurred in 2012 (293.8 mm), with more intense use and occupation of the river basin. In the same year, the area of exposed soil and open DTF was 16 km2 out of a total area of 35.2 km2, representing 45% of the total area of the basin.
The reduction in the volume of the reservoir following the drought in the region [65] led to the population occupying the area of the watershed to make agricultural activity more viable (Figure 11), since during periods of extreme drought, the area of the watershed of a reservoir has the highest humidity. The authors of [25] pointed out that using the land in PPAs for productive purposes is very damaging to the ecosystem and the human population in general, as it affects natural cycles.
The limits of the PPAs around bodies of water, known as riparian forests, are defined in Brazilian legislation. However, the law does not take into account the water balance, rainfall regime, geomorphology, pedology or climate of the environment in which the water body is located, which are essential criteria for such definitions [27,66]. This controversy is the focus of much research [67,68,69,70].

3.5. Correlation Between Water Quality and Land Use in Zones of Riparian Forests

Inappropriate land use, especially on the banks of a water source, can lead to a significant reduction in water quality [71]. The correlations between the limnological parameters of water quality and the occupancy of the areas of maximum (100 m) and minimum (30 m) riparian forest defined by law for the reservoir are shown in Table 5.
The correlations for the 30-m riparian zone were significant and positive between exposed soil and TN and exposed soil and Chl-a, and negative between exposed soil and transparency. The 100-m riparian zone showed a significant positive correlation between dense DTF and transparency, and between TP and TN. This result suggests that for riparian forests at the lower limit of the legislation, areas of exposed soil have a greater impact on the stability of water quality than the presence of vegetation does. The opposite occurs for riparian forests at the upper limit, since the correlations show that for the 100-m riparian zone the presence of vegetation is more important for the water quality of the reservoir.
The retention of materials in water bodies by riparian forests has been widely discussed [27,72,73]. The consensus is that maintaining these areas limits river gradients, reduces and filters surface runoff and makes sediment transport more difficult. The authors of [6] identified that soil exposure in PPAs in a basin in the semi-arid region of Brazil contributes to more than 20% of inappropriate land use, affecting the quality of the water resources and reducing protection against erosion events.

3.6. Multivariate Analysis of the Evaluated Parameters

Figure 12 shows the factor loadings of the principal factors after Varimax rotation, with the position of the variables on the axes of the two factors that were extracted. The values along the axes indicate the factor loadings of the variables for each component. Factor loadings are the correlations between the original variables and the extracted factors [60]. The two factors together explain a significant part of the total variance (93.14%).
Trans, as well as open and dense DTF, had a positive correlation with Component 1, which explains 61.77% of the total variance and forms one group. Component 2, which explains 31.37% of the total variance, has positive values for ES, Chl-a and TN. This suggests that ES showed different behavior in terms of the stability of the water quality, differing from both open and dense DTF. These results are explained mainly by the presence or absence of vegetation in the protected areas, which may intensify sediment transport to the reservoir during rainfall events.
The production of sediments by drag, and the loss of organic matter and nutrients, in watersheds in the semi-arid region of Brazil are closely related to the surface runoff caused by rainfall, as seen by [74]. This process is particularly important in agroecosystems in the region, where the volume of eroded soil regulates the loss of nutrients, directly affecting soil fertility and water quality. In addition, factors such as the natural weathering of the soil, the transport of suspended solids in agricultural areas and anthropogenic action play a crucial role in the composition of the water in semi-arid regions [14].
Furthermore, the leaching of nitrogen from exposed areas in the PPAs helps to increase the amount of chlorophyll-a in the water. Limitations on light or nitrogen availability result in lower-than-expected chlorophyll-a concentrations [75]; the amount of chlorophyll-a is directly related to nitrogen availability, where limitations on the nutrient contribute to lowering the concentrations of the component.
TP showed a weak and negative correlation with both factors and was the most asymmetric limnological variable in the study (2.12) due to the complexity of the dynamics of the phosphorus (P) at the sediment–water interface. The transport of P from the sediment to the water is not uniform, as different forms of P have different mobility, which affects the potential of the sediment to influence the trophic state of the environment [76]. The inorganic P–water fraction shows low concentrations, as this is the form found in the interstitial water of the sediment and is poorly adsorbed [19,77], which increases its mobility at the sediment–water interface.
The dynamics of the P fractions in the sediment of lakes and reservoirs is complex and depends on several factors, such as the characteristics of the sediment and the specific environmental conditions of each system. In addition, environmental variables, such as pH, temperature and dissolved oxygen, play fundamental roles in regulating these exchanges, enhancing or restricting the transport of nutrients to the water column. These interactions, which are still poorly understood, have a direct effect on the exchange between the sediment and the water, highlighting the need for studies that afford a deeper understanding of this relationship [78].

4. Conclusions

Changes in LULC in the PPAs of the watershed, characterized by a reduction in the areas of native vegetation and increase in exposed soil, had a significant negative effect on the water quality of the General Sampaio Reservoir over the eight years of the study. Parameters such as water transparency and chlorophyll-a concentration were particularly affected by erosion and surface runoff.
There was a significant reduction in water quality during the dry season, accompanied by a reduction in ground cover in the PPAs. The vegetation in a DTF is deciduous, exposing the soil due to leaf senescence. Furthermore, the reduced water volume of the General Sampaio Reservoir during this period increased the concentration of nutrients and chlorophyll-a, worsening the quality of the water. These factors highlight the urgent need for adequate soil management during the dry season in order to preserve resources.
The 100-m strip of riparian forest showed the greatest effect from the vegetated areas on the correlation between the physicochemical parameters of the water in the reservoir, demonstrating the influence of the forest in protecting the quality of the water. On the other hand, the minimum strip of 30 m showed a greater correlation between the areas of exposed soil and the increase in chlorophyll-a concentration, in addition to a reduction in water transparency. Therefore, choosing a smaller strip of riparian forest might compromise the filtering capacity and natural protection of the reservoir, favoring eutrophication and degrading the quality of the water.
Principal component analysis made it possible to identify the underlying factors that explain the relationships between the variables under analysis. The results suggest that ES exhibits different behavior in terms of stabilizing water quality compared to ODTF and DDTF. This difference is related to the presence or absence of vegetation in the protection areas, which influences how sediment is transported and affects water quality in the reservoir.
As more than half of the areas covered by the PPAs have been used illegally for years, there is a clear need for greater supervision. To reverse the state of degradation, it is essential to carry out research into strategies for restoring the native vegetation, especially given the impact of these areas on the quality of the surface water, which is crucial for supplying the population of the semi-arid region.

Author Contributions

Conceptualization, F.H.O.d.S., F.B.L. and E.M.d.A.; methodology, F.H.O.d.S., F.B.L., B.G.M.d.C.B. and N.S.V.; software, F.H.O.d.S., F.B.L., B.G.M.d.C.B. and N.S.V.; validation, F.H.O.d.S., F.B.L., I.C.d.S.A., N.R.d.S.L., M.C.P. and E.M.d.A.; formal analysis, F.H.O.d.S., F.B.L., B.G.M.d.C.B., N.S.V.; I.C.d.S.A., N.R.d.S.L., M.C.P., R.R.C., F.T.d.A.A. and E.M.d.A.; investigation, F.H.O.d.S., F.B.L. and E.M.d.A.; resources, F.H.O.d.S., F.B.L. and E.M.d.A.; data curation, F.H.O.d.S., F.B.L. and E.M.d.A.; writing—original draft preparation, F.H.O.d.S. and F.B.L.; writing—review and editing, F.H.O.d.S., F.B.L., B.G.M.d.C.B., N.S.V.; I.C.d.S.A., N.R.d.S.L., M.C.P., R.R.C., F.T.d.A.A. and E.M.d.A.; visualization, F.H.O.d.S., F.B.L., B.G.M.d.C.B., N.S.V.; I.C.d.S.A., N.R.d.S.L., M.C.P., R.R.C., F.T.d.A.A. and E.M.d.A.; supervision, F.B.L.; project administration, F.B.L. and E.M.d.A.; funding acquisition, F.B.L. and E.M.d.A. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed in part by the National Council for Scientific and Technological Development (CNPq), process no 311886/2020-5, process no 420885/2023-4 and process no 316421/2023-5; the Coordination for the Improvement of Higher Education Personnel (CAPES)—Finance Code 001 and the Graduate Program in Agricultural Engineering of the Federal University of Ceará—PPGEA/UFC.

Data Availability Statement

Data requests can be made to the corresponding author.

Acknowledgments

We gratefully acknowledge the support of the National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for funding this research. We also extend our sincere thanks to the Graduate Program in Agricultural Engineering of the Federal University of Ceará (PPGEA/UFC) and the Extension Group on Water and Soil Management in the Semi-Arid Region (MASSA) of the Federal University of Ceará (http://www.massa.ufc.br/, accessed on 18 June 2025) for their valuable collaboration and technical support throughout the development of this study. Additionally, we thank the Ceará Water Resources Management Company (COGERH) for providing valuable technical information.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PPAsPermanent Preservation Areas
Chl-aChlorophyll-a
TNTotal nitrogen
TPTotal phosphorus
TransTransparency
COGERHWater resources management company
MASSAResearch and Extension Group on Water and Soil Management in the Semi-Arid Region of the Federal University of Ceará (UFC)
UFCFederal University of Ceará
OFOpen Forest
DFDense Forest
ESExposed Soil
PCAPrincipal component analysis
DTFDry Tropical Forest
WGSRWatershed of the General Sampaio Reservoir
BSh’w’A type of hot semi-arid climate according to the Köppen classification
FUNCEMECeará Foundation for Meteorology and Water Resources
UTMUniversal Transverse Mercator coordinate system
WGS 84World Geodetic System 1984
DEMDigital Elevation Model
ArcGISA geographic information system software
SRTMShuttle Radar Topography Mission DEM
SPSSA statistical software
LAQAEnvironmental Chemistry Laboratory (of UFC)
APHAAmerican Public Health Association
AWWAAmerican Water Works Association
WEFWater Environment Federation
SMWWStandard Methods for Examination of Water and Wastewater
USGSUnited States Geological Survey 1
TMThematic Mapper sensor (on Landsat 5)
ETM+Enhanced Thematic Mapper Plus sensor (on Landsat 7)
OLIOperational Land Imager sensor (on Landsat 8)
ENVIEnvironment for Visualizing Images software
DNDigital numbers
FLAASHFast Line-of-sight Atmospheric Analysis of Hypercubes algorithm
MODTRANModerate Resolution Atmospheric Transmission Model
ppmParts per million
MAXVERMaximum likelihood method (for land-use classification)
SDStandard deviation
CONAMANational Environment Council (of Brazil)

References

  1. Sarzaeim, O.B.H.; Mehdipour, E.F.; Loáiciga, H.A. Climate change outlook for water resources management in a semiarid river basin: The effect of the environmental water demand. Environ. Earth Sci. 2017, 76, 1–8. [Google Scholar] [CrossRef]
  2. Hamlat, A.; Guidoum, A.; Koulala, I. Status and trends of water quality in the Tafna catchment: A comparative study using water quality indices. J. Water Reuse Desalination 2017, 7, 228–245. [Google Scholar] [CrossRef]
  3. Savage, C.; Leavitt, P.R.; Elmgren, R. Effects of land use, urbanization, and climate variability on coastal eutrophication in the Baltic Sea. Limnol. Oceanogr. 2010, 55, 1033–1046. [Google Scholar] [CrossRef]
  4. Mahessar, A.A.; Qureshi, A.L.; Ursani, H.; Tunio, I.; Kandhro, B.; Memon, S.A. Environmental Concerns for Water Consumption from Polluted Water Bodies in Watershed Area of Sindh Province. J. Pollut. Eff. Control. 2017, 5, 204. [Google Scholar]
  5. Noriega, C.; Araujo, M.; Varona, H.L.; Costa, M.; Calzada, A.; Medeiros, C.; Silva, A.; Portela, L.; Bezerra, D.; Jeronimo, R.; et al. Long-Term Trend of Nitrogen and Phosphorus Transport In 12 Tropical Coastal Watersheds in Northeast Brazil. Quimica Nova 2023, 46, 616–626. [Google Scholar] [CrossRef]
  6. Andrade, Á.D.S.; Ribeiro, S.D.C.A.; Pereira, B.W.D.F.; Bezerra, P.E.S.; Brandão, V.V.P. Hidrográfica do rio Marapanim, nordeste do Pará Conflito de uso do solo em Áreas de Preservação Permanente da Bacia. Rev. Ciência E Natureza 2021, 43, e20. [Google Scholar] [CrossRef]
  7. Sangüesa, C.; Pizarro, R.; Ibañez, A.; Pino, J.; Rivera, D.; García-Chevesich, P.; Ingram, B. Spatial and Temporal Analysis of Rainfall Concentration Using the Gini Index and PCI. Water 2018, 10, 112. [Google Scholar] [CrossRef]
  8. Guerreiro, M.J.S.; Andrade, E.M.; Abreu, I.; Lajinha, T. Long-term variation of precipitation indices in Ceará State, Northeast Brazil. Int. J. Climatol. 2013, 33, 2929–2939. [Google Scholar] [CrossRef]
  9. de Andrade, E.M.; Sena, M.G.T.; da Silva, A.G.R.; Pereira, F.J.S.; Lopes, F.B. Uncertainties of the rainfall regime in a tropical semi-arid region: The case of the State of Ceará. Rev. Bras. Agroambiente 2016, 10, 88–95. [Google Scholar] [CrossRef]
  10. Souza, M.S.; Campos, K.C.; Braga, F.L.P.; Lopes, F.B. Determinantes do regime pluviométrico no Semiárido Cearense (1990–2019). Rev. Bras. Climatologia 2024, 34, 533–556. [Google Scholar] [CrossRef]
  11. Souza, B.I.; Artigas, R.C.; Lima, E.R.V. Caatinga e desertificação. Mercat. Fortaleza 2015, 14, 131–150. [Google Scholar]
  12. Knipper, K.; Hogue, T.; Scott, R.; Franz, K. Evapotranspiration Estimates Derived Using Multi-Platform Remote Sensing in a Semiarid Region. Remote Sens. 2017, 9, 184. [Google Scholar] [CrossRef]
  13. Maia, A.R.S.; Lopes, F.B.; de Andrade, E.M. Influence of Climatic Seasonality on a Survey of Land Use and Cover in the Semi-arid Region. J. Agric. Sci. 2018, 10, 311. [Google Scholar] [CrossRef]
  14. Lopes, F.B.; Andrade, E.M.; Meireles, A.C.M.; Becker, H.; Batista, A.A. Assessment of the water quality in a large reservoir in semiarid region of Brazil. Revista Bras. Eng. Agrícola Ambient. 2014, 18, 437–445. [Google Scholar] [CrossRef]
  15. Praxedes, C.; Lopes, F.; Andrade, E.; Silva, T.; Becker, H. Evaluation of nitrogen and phosphorus in surface reservoirs of the semi-arid region of Brazil using mass balance. Rev. Ciência Agronômica 2023, 54, e20228421. [Google Scholar] [CrossRef]
  16. Lima, A.; Rodal, M.; Castro, C.; Antonino, A.; Melo, A.; Gonçalves-Souza, T.; Sampaio, E. Phenology of high- and low-density wood deciduous species responds differently to water supply in tropical semiarid regions. J. Arid. Environ. 2021, 193, 104594. [Google Scholar] [CrossRef]
  17. Chou, J.-S.; Ho, C.-C.; Hoang, H.-S. Determining quality of water in reservoir using machine learning. Ecol. Inform. 2018, 44, 57–75. [Google Scholar] [CrossRef]
  18. Lima, F.J.D.O.; Lopes, F.B.; Andrade, E.M.; Rocha, F.C.; Meireles, A.C.M. Spatio-Temporal Dynamics of Toxic Cyanobacteria in an Artificial Lake in the Brazilian Semi-Arid Region. Rev. Caatinga 2022, 35, 423–435. [Google Scholar] [CrossRef]
  19. Cavalcante, H.; Cruz, P.S.; Viana, L.G.; de Lucena-Silva, D.; de Lucena Barbosa, J.E. Influence of the use and the land cover of the catchment in the water quality of the semiarid tropical reservoirs. J. Hyperspectr. Remote Sens. 2018, 7, 389–398. [Google Scholar] [CrossRef]
  20. Pereira, E.C.B.; Lopes, F.B.; de Almeida, A.M.M.; de Andrade, E.M.; Lopes, J.F.B. Sedimentos e Nutrientes Aportados a Um Reservatório de Pequeno Porte No Semiárido Tropical. Rev. Cienc. Agron. 2021, 52, e20196717. [Google Scholar] [CrossRef]
  21. Chaves, L.C.G.; Lopes, F.B.; Maia, A.R.S.; Meireles, A.C.M.; de Andrade, E.M. Water quality and anthropogenic impact in the watersheds of service reservoirs in the Brazilian semi-arid region. Rev. Cienc. Agron. 2019, 50, 223–233. [Google Scholar] [CrossRef]
  22. Duarte, M.R.N.; Pereira, T.M.; Lima, P.d.F.; Pereira, E.C.B.; Lopes, F.B.; Rezende, C.F. Limnological dynamics in artificial reservoir and intermittent river in the semiarid region depending on land use and occupation. Rev. Cienc. Agron. 2021, 52, 1–10. [Google Scholar] [CrossRef]
  23. Lima, F.J.d.O.; Lopes, F.B.; Cid, D.A.C.; Neto, I.E.L.; Rocha, R.V.; Estácio, A.B.S.; Araújo, I.C.d.S.; Luna, N.R.d.S.; Pontes, M.C.; de Souza, A.C.T.; et al. Determination of the Total Phosphorus Decay Coefficient Based on Hydrological Models in an Artificial Reservoir in the Brazilian Semi-Arid Region. Hydrology 2025, 12, 36. [Google Scholar] [CrossRef]
  24. Coelho, R.C.R.P.; Buffon, I.; Guerra, T. Influência do uso e ocupação do solo na qualidade da água: Um método para avaliar a importância da zona ripária. Ambiente Água 2011, 6, 104–117. [Google Scholar] [CrossRef]
  25. Cocco, J.; dos Santos Galvanin, E.A.; Ribeiro, H.V.; de Lima Nascimento, D. Land Use/Land Cover Analysis in the Permanent Preservation Areas at the Springs of the Sub-Basin from Mato Grosso State–Brazil. Ciência Nat. 2016, 38, 1411–1418. [Google Scholar] [CrossRef]
  26. Wong, W.W.; Pottage, J.; Warry, F.Y.; Reich, P.; Roberts, K.L.; Grace, M.R.; Cook, P.L.M. Stable isotopes of nitrate reveal different nitrogen processing mechanisms in streams across a land use gradient during wet and dry periods. Biogeosciences 2018, 15, 3953–3965. [Google Scholar] [CrossRef]
  27. Campagnolo, K.; da Silveira, G.L.; Miola, A.C.; da Silva, R.L.L. Área de Preservação Permanente de um rio e análise da legislação de proteção da vegetação nativa. Ciênc. Florest. 2017, 27, 831–842. [Google Scholar] [CrossRef]
  28. Lopes, F.B.; de Andrade, E.M.; Teixeira, A.D.S.; Caitano, R.F.; Chaves, L.C.G. Uso de geoprocessamento na estimativa da perda de solo em microbacia hidrográfica do semiárido brasileiro. Rev. Bras. Agroambiente 2011, 5, 88–96. [Google Scholar] [CrossRef]
  29. Pontes, N.S.C.; Noronha, S.; Silva, F.H.O.; Amaral, J.B.C.; Lopes, F.B. Estimating concentrations of chlorophyll-a and suspended sediments in a continental aquatic ecosystem using remote sensing. Rev. Bras. Agroambiente 2020, 14, 1–13. [Google Scholar]
  30. Lopes, F.B.; Barbosa, C.C.F.; Novo, E.M.L.d.M.; de Carvalho, L.A.S.; de Andrade, E.M.; Teixeira, A.d.S. Modeling the concentration of chlorophyll-a in a continental aquatic ecosystem of the Brazilian seminar based on remote sensing. Rev. Cienc. Agron. 2021, 52, 1–12. [Google Scholar] [CrossRef]
  31. Hoff, R.; Pauletto, H.; Alberti, R.; Farias, A.R. Sustainable Viticulture: Geotechnologies for Update Permanent Preservation Areas in the Serra Gaúcha Wine Region, Brazil. Am. J. Environ. Eng. 2016, 6, 167–174. [Google Scholar]
  32. Hansen, C.H.; Burian, S.J.; Dennison, P.E.; Williams, G.P. Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models. Remote Sens. 2017, 9, 409. [Google Scholar] [CrossRef]
  33. Lopes, F.B.; Barbosa, C.C.F.; Novo, E.; Andrade, E.M.; Chaves, L.C.G. Modelagem da qualidade das águas a partir de sensoriamento remoto hiperespectral. Rev. Bras. Eng. Agríc. Ambient. 2014, 18, 13–19. [Google Scholar] [CrossRef]
  34. da Silva, D.E.P.R. On climate variability in Northeast of Brazil. J. Arid Environ. 2004, 58, 574–596. [Google Scholar] [CrossRef]
  35. Campos, D.A.; De Andrade, E.M. Seasonal trend of climate variables in an area of the Caatinga phytogeographic domain. Rev. Bras. Agroambiente 2021, 15, 1–18. [Google Scholar] [CrossRef]
  36. de Jesus, J.B.; de Oliveira, D.G.; Araújo, W.S.; da Cruz, L.S.; Kuplich, T.M. Influence of anthropization on the floristic composition and phytosociology of the Caatinga susceptible to desertification in the state of Sergipe, Brazil. Trop. Ecol. 2022, 63, 398–408. [Google Scholar] [CrossRef]
  37. dos Santos, R.H.S.; Lima, J.R.d.S.; de Oliveira, C.L.; Souza, R.M.S.; Antonino, A.C.D.; de Souza, E.S. Variação sazonal e interanual da umidade do solo e da evapotranspiração em Caating. Rev. Bras. Geogr. Física 2023, 16, 2390–2409. [Google Scholar] [CrossRef]
  38. Barboza, E.N.; Caiana, C.R.A.; Neto, F.d.C.B. Análise da precipitação pluviométrica na Região do Centro-Sul Cearense: Um estudo do período (1980–2009). Res. Soc. Dev. 2020, 9, e18963304. [Google Scholar] [CrossRef]
  39. Júnior, M.P.D.; Marangon, L.C.; Gonçalves, M.d.P.M.; Feliciano, A.L.P. Phytosociological analysis and ecological indicators in two areas of Caatinga with different historics of use in the hills of Paraíba. Cienc. Florest. 2022, 32, 1439–1459. [Google Scholar] [CrossRef]
  40. American Public Health Association; American Water Works Association; Water Environment Federation. Standard Methods for the Examination of Water and Wastewater, 21st ed.; APHA/AWWA/WEF: Washington, DC, USA, 2005. [Google Scholar]
  41. Yuan, J.; Niu, Z. Evaluation of atmospheric correction using FLAASH. In Proceedings of the International Workshop on Earth Observation and Remote Sensing Applications, Beijing, China, 30 June–2 July 2008; pp. 1–6. [Google Scholar] [CrossRef]
  42. Jia, L.; Limin, W.; Lingbo, Y.; Fei, T.; Jie, S.; Fugang, Y.; Changhong, F. GF-1 satellite image atmospheric correction based on 6S model and its effect. Trans. Chin. Soc. Agric. Eng. 2015, 31, 159–168. [Google Scholar]
  43. Vale, J.R.B.; Costa, J.A.; dos Santos, J.F.; da Silva, E.L.S.; Favacho, A.T. Análise comparativa de métodos de classificação supervisionada aplicada ao mapeamento da cobertura do solo no município de Medicilândia, Pará. InterEspaço 2018, 4, 26–44. [Google Scholar] [CrossRef]
  44. Oliveira, G.C.; Filho Fernandes, E.I. Metodologia para delimitação de APP’s em topos de morros segundo o novo Código Florestal brasileiro utilizando sistemas de informação geográfica. In Proceedings of the Anais XVI Simpósio Brasileiro de Sensoriamento Remoto—SBSR, Foz do Iguaçu, Brazil, 13–18 April 2013. [Google Scholar]
  45. de Mendonça, J.C.; Lopes, F.B.; de Andrade, E.M.; Praxedes, C.F.; Lima, F.J.d.O.; da Silva, F.H.O. Qualitative vulnerability of the waters of a surface reservoir subjected to drought in a tropical semi-arid region1. Rev. Cienc. Agron. 2023, 54, e20207803. [Google Scholar] [CrossRef]
  46. da Silva, D.B.; Bellotto, V.R.; Barbosa, J.D.S.B.; Lima, T.B. Spatiotemporal Variation on Water Quality and Trophic State of a Tropical Urban Reservoir: A Case Study of the Lake Paranoá-DF, Brazil. Water 2021, 13, 3314. [Google Scholar] [CrossRef]
  47. Coelho, C.; Heim, B.; Foerster, S.; Brosinsky, A.; De Araújo, J.C. In Situ and Satellite Observation of CDOM and Chlorophyll-a Dynamics in Small Water Surface Reservoirs in the Brazilian Semiarid Region. Water 2017, 9, 913. [Google Scholar] [CrossRef]
  48. Aranha, T.R.B.T.; Martinez, J.-M.; Souza, E.P.; Barros, M.U.G.; Martins, E.S.P.R. Remote Analysis of the Chlorophyll-a Concentration Using Sentinel-2 MSI Images in a Semiarid Environment in Northeastern Brazil. Water 2022, 14, 451. [Google Scholar] [CrossRef]
  49. Ventura, D.L.T.; Martinez, J.-M.; De Attayde, J.L.; Martins, E.S.P.R.; Brandini, N.; Moreira, L.S. Long-term series of chlorophyll-a concentration in Brazilian semiarid lakes from MODIS imagery. Water 2022, 14, 400. [Google Scholar] [CrossRef]
  50. Rocha, F.C.; Andrade, E.M.; Lopes, F.B. Water quality index calculated from biological, physical and chemical attributes. Environ. Monit. Assess. 2015, 187, 4163. [Google Scholar] [CrossRef] [PubMed]
  51. Busse, L.B.; Simpson, J.C.; Cooper, S.D. Relationships among nutrients, algae, and land use in urbanized southern California streams. Can. J. Fish. Aquat. Sci. 2006, 63, 2621–2638. [Google Scholar] [CrossRef]
  52. Zhang, Z.; Chen, Y.; Wang, P.; Shuai, J.; Tao, F.; Shi, P. River discharge, land use change, and surface water quality in the Xiangjiang River, China. Hydrol. Process. 2013, 28, 4130–4140. [Google Scholar] [CrossRef]
  53. Guidotti, V.; Ferraz, S.F.d.B.; Pinto, L.F.G.; Sparovek, G.; Taniwaki, R.H.; Garcia, L.G.; Brancalion, P.H. Changes in Brazil’s Forest Code can erode the potential of riparian buffers to supply watershed services. Land Use Policy 2020, 94, 104511. [Google Scholar] [CrossRef]
  54. ElKadiri, R.; Momm, H.G.; Bingner, R.L.; Moore, K. Spatial Optimization of Conservation Practices for Sediment Load Reduction in Ungauged Agricultural Watersheds. Soil Syst. 2023, 7, 4. [Google Scholar] [CrossRef]
  55. Pontes Filho, J.D.; Souza Filho, F.d.A.; Martins, E.S.P.R.; Studart, T.M.d.C. Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil. Water 2020, 12, 834. [Google Scholar] [CrossRef]
  56. Silva, M.A.O.; dos Santos Liporace, F. Detecção automática de nuvem e sombra de nuvem em imagens de sensoriamento remoto. Bol. Ciênc. Geod. 2016, 22, 369–388. [Google Scholar] [CrossRef]
  57. Li, S.; Song, K.; Wang, S.; Liu, G.; Wen, Z.; Shang, Y.; Lyu, L.; Chen, F.; Xu, S.; Tao, H.; et al. Quantificação de clorofila-a em lagos típicos em toda a China usando imagens Sentinel-2 MSI com algoritmo de aprendizado de máquina. Sci. Total Environ. 2021, 778, 146271. [Google Scholar]
  58. de Andrade, E.M.; Ferreira, K.C.D.; Lopes, F.B.; Araújo, I.C.d.S.; da Silva, A.G.R. Balance of nitrogen and phosphorus in a reservoir in the tropical semiarid region. Rev. Cienc. Agron. 2020, 51, e20196800. [Google Scholar] [CrossRef]
  59. Huang, Y.; Lou, C.; Luo, L.; Wang, X. Insight into the effects of magnesium and phosphorus on the mixotrophic growth of Chlorella vulgaris under stably controlled nutrient conditions. Sci. Total Environ. 2021, 752, 141747. [Google Scholar] [CrossRef] [PubMed]
  60. Menezes, J.P.C.; Bittencourt, R.P.; Farias, M.S.; Bello, I.P.; Fia, R.; Oliveira, L.F.C. Relação entre padrões de uso e ocupação do solo e qualidade da água em uma bacia hidrográfica urbana. Eng. Sanit. Ambient. 2016, 21, 519–534. [Google Scholar] [CrossRef]
  61. Yin, J.; Liu, H.; Chen, X. Dynamic Change in the Water-Level Fluctuation Zone of the Danjiangkou Reservoir and Its Influence on Water Quality. Sustainability 2018, 10, 1025. [Google Scholar] [CrossRef]
  62. Butt, M.J.; Mahmood, R.; Waqas, A. Deposição de sedimentos devido à erosão do solo na região da bacia hidrográfica da Barragem de Mangla. Environ. Monit. Assess. 2011, 181, 419–429. [Google Scholar] [CrossRef]
  63. Zheng, Y.; Luo, X.; Zhang, W.; Wu, X.; Zhang, J.; Han, F. Transport mechanisms of soil-bound mercury in the erosion process during rainfall-runoff events. Environ. Pollut. 2016, 215, 10–17. [Google Scholar] [CrossRef]
  64. Ribeiro, T.G.; Boaventura, G.R.; da Cunha, L.S.; Pimenta, S.M. Estudo da qualidade das águas por meio da correlação de parâmetros físico-químicos, bacia hidrográfica do Ribeirão Anicuns. Geochim. Bras. 2016, 30, 84–94. [Google Scholar]
  65. Marengo, J.A.; Alves, L.M.; Soares, W.R.; Rodriguez, D.A.; Camargo, H.; Riveros, M.P.; Pabló, A.D. Two Contrasting Severe Seasonal Extremes in Tropical South America in 2012: Flood in Amazonia and Drought in Northeast Brazil. J. Clim. 2013, 26, 9137–9154. [Google Scholar] [CrossRef]
  66. Domingues, A.L.; Nissinen-Lipp, K.H.; Mirandas, L.S.; Burioli, G.A. Delimitação da área de preservação permanente da Lagoa dos Gateados, na planície costeira do Rio Grande do Sul (RS), utilizando séries de imagens de satélite e dados hidrológicos históricos. Rev. Bras. Geogr. Fís. 2015, 8, 776–792. [Google Scholar] [CrossRef]
  67. Reis, R.B.; Cardoso, P.; Cruz, C.B.M.; Vicens, R.S.M. Mapeamento e caracterização das Áreas de Preservação Permanente (APP’s) na Área de Proteção Ambiental do Rio São João/Mico Leão Dourado. In Proceedings of the Anais XIV Simpósio Brasileiro de Sensoriamento Remoto, Natal, Brazil, 25–30 April 2009. [Google Scholar]
  68. Schäffer, W.B. Permanent Preservation Areas and Conservation Units vs. Risk Areas: What Does One Have to Do with the Other? 2nd ed.; Ministry of the Environment (Brazil), Secretariat of Biodiversity and Forests, and Secretariat of Water Resources and Urban Environment: Brasília, Brazil, 2013; Biodiversity Series, No. 41; ISBN 9788577381555. [Google Scholar]
  69. Campagnolo, K.; Kobiyama, M.; Fagundes, M.R.; De Menezes, D.; Iroumé, A.; Michel, G.P.; Rodrigues, M.F. Influence of large wood dynamics on flow and channel morphology in a forest stream. Geomorphology 2024, 459, 109268. [Google Scholar] [CrossRef]
  70. Ramos, L.; Negreiros, D.; Goulart, F.F.; Figueiredo, J.C.; Gomes, K.; Siqueira, W.T.; Justino, T.S.P.; Maia, R.O. Dissimilar forests along the Rio Doce watershed call for multiple restoration references to avoid biotic homogenization. Sci. Total Environ. 2024, 930, 172720. [Google Scholar] [CrossRef] [PubMed]
  71. Tsujii, K.P.; Ribeiro, A.C.C.; Carneiro, A.; Neto Silva, C.M.; Gonçalves, B.B. Uso e ocupação das áreas de preservação permanentes no sudoeste Goiano. Rev. Geogr. 2014, 31, 55–64. [Google Scholar]
  72. Neves, C.B.; Castro, S.S.; Santos, N.; Borges, R.O. Análise das relações entre solos, relevo e a legislação ambiental para a delimitação das Áreas de Preservação Permanente: O exemplo da alta bacia do ribeirão João Leite, Estado de Goiás. Rev. Bras. Geomorfol. 2009, 10. [Google Scholar] [CrossRef]
  73. Salemi, L.F.; Groppo, J.D.; Trevisan, R.; Moraes, J.M.; Lima, W.P.; Martinelli, L.A. Aspectos hidrológicos da recuperação florestal de Áreas de Preservação Permanente ao longo dos corpos da água. Rev. Inst. Florestal 2011, 23, 69–80. [Google Scholar] [CrossRef]
  74. Freitas, F.R.S.; Righetto, A.M.; Attayde, J.L. Cargas de Fósforo Total e Material em Suspensão em um Reservatório do Semi-Árido Brasileiro. Oecologia Aust. 2011, 15, 655–665. [Google Scholar] [CrossRef]
  75. Freitas, M.A.S.R.; Andrade, E.M.; Weber, O.B.; Palácio, H.A.Q.; Ferreira, T.O. Cargas de sedimentos e perdas de nutrientes em agroecossistemas do semiárido brasileiro. Nutr. Cycl. Agroecosyst. 2013, 96, 203–213. [Google Scholar] [CrossRef]
  76. Wang, H.; Holden, J.; Spera, K.; Xu, X.; Wang, Z.; Luan, J.; Xu, X.; Zhang, Z. Phosphorus fluxes at the sediment–water interface in subtropical wetlands subjected to experimental warming: A microcosm study. Chemosphere 2013, 90, 1794–1804. [Google Scholar] [CrossRef] [PubMed]
  77. Tye, A.M.; Rushton, J.; Vane, C.H. Distribution and speciation of phosphorus in foreshore sediments of the Thames estuary, UK. Mar. Pollut. Bull. 2018, 127, 182–197. [Google Scholar] [CrossRef] [PubMed]
  78. Moura, D.S.; Neto, I.E.L.; Clemente, A.; Oliveira, S.; Pestana, C.J.; de Melo, M.A.; Capelo-Neto, J. Modelagem da troca de fósforo entre sedimentos de fundo e água em regiões tropicais reservatórios semiáridos. Chemosphere 2020, 246, 125686. [Google Scholar] [CrossRef]
Environments 12 00220 g001
Figure 1. Location of the watershed of the General Sampaio Reservoir in the state of Ceará, Brazil.
Figure 1. Location of the watershed of the General Sampaio Reservoir in the state of Ceará, Brazil.
Environments 12 00220 g001
Environments 12 00220 g002
Figure 2. Overall flowchart of the different activities.
Figure 2. Overall flowchart of the different activities.
Environments 12 00220 g002
Environments 12 00220 g003
Figure 3. Daily and accumulated rainfall and the water volume stored in the General Sampaio Reservoir, from 2009 to 2016.
Figure 3. Daily and accumulated rainfall and the water volume stored in the General Sampaio Reservoir, from 2009 to 2016.
Environments 12 00220 g003
Environments 12 00220 g004
Figure 4. Flowchart of the activities to determine strips of riparian forest in the General Sampaio Reservoir.
Figure 4. Flowchart of the activities to determine strips of riparian forest in the General Sampaio Reservoir.
Environments 12 00220 g004
Environments 12 00220 g005
Figure 5. Variation in the water quality parameters of the General Sampaio Reservoir.
Figure 5. Variation in the water quality parameters of the General Sampaio Reservoir.
Environments 12 00220 g005
Environments 12 00220 g006
Figure 6. Boxplot of limnological data from the General Sampaio Reservoir: transparency (A), total phosphorus (B), chlorophyll-a (C) and total nitrogen (D).
Figure 6. Boxplot of limnological data from the General Sampaio Reservoir: transparency (A), total phosphorus (B), chlorophyll-a (C) and total nitrogen (D).
Environments 12 00220 g006
Environments 12 00220 g007
Figure 7. Map of the delimited Permanent Preservation Areas (PPAs) in the watershed of the General Sampaio Reservoir.
Figure 7. Map of the delimited Permanent Preservation Areas (PPAs) in the watershed of the General Sampaio Reservoir.
Environments 12 00220 g007
Environments 12 00220 g008
Figure 8. Variation in LULC in the PPAs of the watershed of the General Sampaio Reservoir between 2009 and 2016.
Figure 8. Variation in LULC in the PPAs of the watershed of the General Sampaio Reservoir between 2009 and 2016.
Environments 12 00220 g008
Environments 12 00220 g009
Figure 9. Boxplot of LULC class areas within the PPAs of the General Sampaio Reservoir watershed.
Figure 9. Boxplot of LULC class areas within the PPAs of the General Sampaio Reservoir watershed.
Environments 12 00220 g009
Environments 12 00220 g010
Figure 10. Qualitive representation of evolution of LULC of riparian zone (100 m) delimitation of the General Sampaio Reservoir and temporal reduction in water volume (2009–2016).
Figure 10. Qualitive representation of evolution of LULC of riparian zone (100 m) delimitation of the General Sampaio Reservoir and temporal reduction in water volume (2009–2016).
Environments 12 00220 g010
Environments 12 00220 g011
Figure 11. Occupation of the area of the watershed of the General Sampaio Reservoir with (A) agriculture, (B) agriculture and livestock, (C,D) fish farming and (E,F) livestock.
Figure 11. Occupation of the area of the watershed of the General Sampaio Reservoir with (A) agriculture, (B) agriculture and livestock, (C,D) fish farming and (E,F) livestock.
Environments 12 00220 g011
Environments 12 00220 g012
Figure 12. Principal component analysis for the qualitative parameters of water and land use in the PPAs from 2009 to 2016 in a watershed in the semi-arid region. Trans (transparency), TN (total nitrogen), TP (total phosphorus), Chl-a (chlorophyll-a) and ES (exposed soil).
Figure 12. Principal component analysis for the qualitative parameters of water and land use in the PPAs from 2009 to 2016 in a watershed in the semi-arid region. Trans (transparency), TN (total nitrogen), TP (total phosphorus), Chl-a (chlorophyll-a) and ES (exposed soil).
Environments 12 00220 g012
Table 1. Dates of the water quality data collections and image acquisition.
Table 1. Dates of the water quality data collections and image acquisition.
PeriodWater
Quality Data		Reservoir
Volume (%)		ImagesReservoir Volume (%)
2009RainyJanuary34.9June100
DryAugust99.3August99.3
2010RainyMay78.2June75.8
DryNovember58.3October61.4
2011RainyMay61.7April58.5
DryAugust58.2August58.2
2012RainyJuly37.8May39.8
DryAugust32.9June35.2
2013RainyMay19.8May19.8
DryAugust16.2September13.9
2014RainyMay5.1June4.6
DryOctober3.6September3.99
2015RainyApril3.9January2.8
DrySeptember3.1October2.8
2016RainyApril3.0June2.8
DrySeptember2.3August2.6
Table 2. Width of the Permanent Preservation Area based on the width of the drainage channel (Law No. 12.727, 2012).
Table 2. Width of the Permanent Preservation Area based on the width of the drainage channel (Law No. 12.727, 2012).
Width of the Drainage ChannelWidth of the PPA Along Each Margin
up to 10 m30 m
from 10 to 50 m50 m
from 50 to 200 m100 m
from 200 to 600 m200 m
greater than 600 m500 m
Table 3. Sequence of functions used to delimit the Permanent Preservation Areas on hills and mountains in the WGSR.
Table 3. Sequence of functions used to delimit the Permanent Preservation Areas on hills and mountains in the WGSR.
Sequential Functions
1. Focal statistics2. Fill3. Minus4. Flow direction5. Basin6. Raster to polygon
7. Feature to line8. Zonalstatistics9. Raster calculator10. Reclassify11. Raster to point12. Add surface information
13. Zonal statistics14. Raster calculator15. Reclassify16. Raster to point17. Add surface information18. Generate near table
19. Join field20. Join field21. Add field22. Calculate field23. Spatial join24. Polygon to raster
25. Raster calculator26. Reclassify27. Raster to polygon28. Zonal statistics29. Raster calculator30. Zonal statistics
31. Reclassify32. Times33. Raster calculator34. Zonal statistics35. Raster calculator36. Times
Table 4. Correlation between the limnological variables TRANS, Cl-a, TN and TP of the General Sampaio Reservoir and the data for the ES, open DTF and dense DTF classes in the area surrounding the General Sampaio Reservoir, from 2009 to 2016.
Table 4. Correlation between the limnological variables TRANS, Cl-a, TN and TP of the General Sampaio Reservoir and the data for the ES, open DTF and dense DTF classes in the area surrounding the General Sampaio Reservoir, from 2009 to 2016.
Cl-aTransTNTP
ES0.553 *−0.574 *0.3710.335
Open DTF−0.426 *0.212−0.385−0.386
Dense DTF−0.2440.210−0.071−0.274
* Correlation significant at a level of 0.05.
Table 5. Spearman correlation coefficients between total nitrogen, total phosphorus, transparency and chlorophyll-a and land use and occupation in 30 and 100 m strips of riparian forest in the General Sampaio Reservoir.
Table 5. Spearman correlation coefficients between total nitrogen, total phosphorus, transparency and chlorophyll-a and land use and occupation in 30 and 100 m strips of riparian forest in the General Sampaio Reservoir.
Strip of Riparian Forest (m)ClassTransparencyPhosphorusNitrogenChlorophyll-a
30Exposed soil−0.724 **0.2450.538 *0.609 **
Open DTF0.0680.145−0.232−0.385
Dense DTF0.252−0.312−0.206−0.079
100Exposed soil−0.129−0.0970.1440.065
Open DTF−0.229−0.066−0.82−0.009
Dense DTF0.458 *−0.521 *−0.487 *−0.321
** Correlation significant at a level of 0.01. * Correlation significant at a level of 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Silva, F.H.O.d.; Lopes, F.B.; Bezerra, B.G.M.d.C.; Viana, N.S.; Araújo, I.C.d.S.; Luna, N.R.d.S.; Pontes, M.C.; Cavalcante, R.R.; Aragão, F.T.d.A.; Andrade, E.M.d. Impact of Permanent Preservation Areas on Water Quality in a Semi-Arid Watershed. Environments 2025, 12, 220. https://doi.org/10.3390/environments12070220

AMA Style

Silva FHOd, Lopes FB, Bezerra BGMdC, Viana NS, Araújo ICdS, Luna NRdS, Pontes MC, Cavalcante RR, Aragão FTdA, Andrade EMd. Impact of Permanent Preservation Areas on Water Quality in a Semi-Arid Watershed. Environments. 2025; 12(7):220. https://doi.org/10.3390/environments12070220

Chicago/Turabian Style

Silva, Fernanda Helena Oliveira da, Fernando Bezerra Lopes, Bruno Gabriel Monteiro da Costa Bezerra, Noely Silva Viana, Isabel Cristina da Silva Araújo, Nayara Rochelli de Sousa Luna, Michele Cunha Pontes, Raí Rebouças Cavalcante, Francisco Thiago de Alburquerque Aragão, and Eunice Maia de Andrade. 2025. "Impact of Permanent Preservation Areas on Water Quality in a Semi-Arid Watershed" Environments 12, no. 7: 220. https://doi.org/10.3390/environments12070220

APA Style

Silva, F. H. O. d., Lopes, F. B., Bezerra, B. G. M. d. C., Viana, N. S., Araújo, I. C. d. S., Luna, N. R. d. S., Pontes, M. C., Cavalcante, R. R., Aragão, F. T. d. A., & Andrade, E. M. d. (2025). Impact of Permanent Preservation Areas on Water Quality in a Semi-Arid Watershed. Environments, 12(7), 220. https://doi.org/10.3390/environments12070220

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop