Contamination Vulnerability Assessment of the Bambuí Karst Aquifer, in the Terra Ronca Region - Goiás, Brazil

Contamination Vulnerability Assessment of the Bambuí Karst Aquifer, in the Terra Ronca Region - Goiás, Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contamination Vulnerability Assessment of the Bambuí Karst Aquifer, in the Terra Ronca Region - Goiás, Brazil Ana Karolyna Nunes Amaral, Gisele Bispo Silva, Lucas Espíndola Rosa, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2114089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Karstic environments are characterized as areas of intense rock dissolution, which allows the generation of several typical features, such as sinkholes, resurgences, caves, and dolines. The spatial distribution of these features is of great importance to identify the contamination vulnerability of karst aquifers and serve as a basis for the application of assessment methods. This study aimed to identify and classify the areas vulnerable to underground contamination in the karstic region of the Terra Ronca State Park (PETeR) and its surroundings. The method chosen was the COP, which assesses the physical conditions of the environment (relief, lithology, soils, precipitation, karst features, and vegetation) to determine the areas naturally vulnerable to contamination. The analyses were performed in a GIS environment. The results indicated five categories of areas vulnerable to groundwater contamination: Very High to High (3.14%), Moderate (16.21%), and Low to Very Low (80.62%). The Terra Ronca State Park was the analysis unit with the highest vulnerability in the region (9.91%), due to the concentration of karstic features in the area. The acquired results aim to help in the underground water resources conservation as well as the speleological patrimony. Intrinsic Vulnerability Karst Aquifers Geoprocessing Environmental Zoning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Aquifers are all rock bodies or formations capable of storing and transmitting water. This capacity is specific to each lithotype, that is, the capacity to store (porosity) and transmit water (permeability), the geological factors result in interaction with the rock body since its formation. Thus, each lithological type brings its own hydrogeological characteristics when it is formed, and in turn reacts in its own way to the mitigating factors (Guerra 1986 ). Karst aquifers result from the solubilizing action of water on carbonate rocks. In the karstification process there is a basic mechanism that is the dissolution by water of a carbonate rock (soluble). Particular to karst are the entries of surface water in localized conduits, called sinkholes. This localized mode of seepage is due to the great spatial permeability variability and seepage capacity, which is much higher in karstic environments than in other permeable environments (Santos et al. 2010). Thus, karst areas are of great economic and hydrogeological interest because of their significant groundwater reserves and mineral occurrence, which is used both in construction and agriculture as well as in tourism (Soedwiwahjono and Pamardhi-utomo 2020 ). They have a naturally high degree of vulnerability, and depending on the activities performed on this environment, groundwater is increasingly prone to pollution, given the supply of populations, irrigation, and industry (Ribeiro et al. 2016 ). In this sense, studies focused on the karst aquifers vulnerability theme are of fundamental importance, as they help in activities that aim to protect the recharge areas and, consequently, preserve the good quality of the waters, in face of the technical and financial limitations resulting from the possible contaminations remediation. Furthermore, they allow the understanding of the different degrees of vulnerability of aquifers, being used as a management tool for local and regional development (Santos et al., 2010; Ribeiro et al. 2016 ). The vulnerability concept can be understood, according to Foster and Hirata (1988), as the set of intrinsic characteristics of the strata between the saturated zone and the soil surface, which determines their susceptibility to suffer the adverse contaminant load effects. Over the years, several methods for assessing the aquifers contamination vulnerability have emerged, among these, the most widely used were: DRAS-TIC (Aller et al. 1985); AVI (Stempvoort et al. 1993 ); GOD (Foster and Hirata, 1988); EPIK (Doerfliger and Zwahlen, 1997); COP (Vías et al. 2006 ). The COP method was developed by Vías et al. ( 2002 ) and is based on the premises of the European COST Action 620 on the assessment of aquifer contamination vulnerability (Daly et al. 2002 ). It was initially applied to carbonate aquifers in southern Spain, Sierra do Libar and Torremolinos, where it showed good results in comparison with other methods (AVI, GOD and DRASTIC) (Vías et al. 2006 ). The main advantages in relation to other methods, is that it can be applied in different climatic conditions, and in different types of carbonate aquifers, as well as in environments that are not fully karstic, considering different weights in the methodology application for these regions. This method has already been applied in several locations, such as the Lez karst system in France, the Kaibab Plateau in Arizona, USA, and the Lagoa Santa karst in Minas Gerais, Brazil. In all cases, satisfactory analyses were presented in the indication of areas vulnerable to underground contamination, demonstrating the possibility of application in different climatic regions (Marín et al. 2012 ; Tayer and Velásques, 2017; Jones et al. 2019 ). The Terra Ronca State Park (PETeR) region and surroundings is located over an extensive karst area and is known for having one of the largest concentrations of caves in Goiás State and in Brazil, being highly exploited by tourism. These environments are extremely fragile, and need an active management, so as to mitigate possible environmental damage (Parise et al. 2015 ), since it is under intense anthropic pression by agricultural activities that are developed in its surroundings, as well as by the morphodynamics of the Serra Geral zone, capable of providing large amounts of sediments to the karstic conduits (Faquim et al. 2017; Aleixo et al. 2019). Rosenberger (2013) points out that the delimitation of naturally vulnerable areas is a widely used technique for the groundwater protection, aiming to reconcile anthropic activities and the terrain capacity to support them environmentally. Considering the extent of carbonate rocks in the study area and the lack of studies on the aquifers vulnerability in the region, the objective of this paper is to assess the intrinsic vulnerability of the Bambuí Aquifer to groundwater contamination in the Terra Ronca region, Goiás State, through the COP method. Materials And Methods Study Area A The study area is located in the headwaters of the right-hand tributaries of the Paranã River, a tributary of the eastern portion of the Tocantins River Basin, specifically near the Tocantins/São Francisco divider, in Central Brazil (Fig. 1 ). This area covers part of the micro-region of the Vão do Paranã, known as one of the three main centers of the Cerrado biome endemism (Felfili et al. 2005 ), comprising the watersheds of the São Domingos, São Vicente, São Mateus, and Água Quente rivers. The region is located on the western border of the São Francisco Craton, where Cretaceous sedimentary rocks of the Urucuia Group, Paleozoic meta-sedimentary rocks of the Santa Fé Group, among others emerge, (Fig. 1 ), mainly Neoproterozoic meta-sedimentary and meta-bearing rocks of the Bambuí Group. The Bambuí Group is formed by two rocky successions, the first, basal marine, is composed of the Sete Lagoas, Serra de Santa Helena, Lagoa do Jacaré and Serra da Saudade formations, which compose the Paraopeba Subgroup. The second succession is the marine-continental one represented by the Três Marias Formation, predominantly psammite. The area is known for the presence of karst geological features such as dolines, lapis, sinkholes, and caves (Mauro et al. 1982; Iglesias and Uhlein, 2009 ; Jansen et al. 2012 ). In the eastern part of the study area, the presence of the Serra Geral de Goiás escarpments stands out. These form the western edge of the Tocantins/São Francisco plateaus, marking the limit between the states of Goiás and Bahia. This eastern region is marked by the presence of sandstones of the Urucuia Group, which are aquifers that serve as springs for some of the main tributaries of the Tocantins River Basin eastern margin (CPRM 2012 ; Faquim et al. 2017). The region climate is classified as tropical sub-humid (Aw), according to the Köppen climate classification, marked by two well-defined seasons. The use and vegetation cover of the region, according to the mapping of land use performed by the MapBiomas project in 2020, are dominated by the Savanna Formation (41.14%), pasture (28.04%), Campestre Formation (15.96%) and agriculture (7.46%), (Souza et al. 2020 ). The soils with greater representation in the study area were Quartz Neosols, with a coverage of 1728.71 km², corresponding to 43.21% of the area, followed by the Cambisolos Háplicos, 27.81%, and Red Argissolos, 14.95% (Mauro et al. 1982; Rosa 2016 ). Within the study area there are two conservation units, one of full protection, the Terra Ronca State Park (PETeR) and another of sustainable use, the Serra Geral Environmental Protection Area (APA Serra Geral). The PETeR was created on July 7, 1989, by Law Nº 10.879, with its main objective being "to preserve the flora, fauna, springs and, in particular, the areas of underground natural cavities occurrence and their surroundings, found in the São Domingos Municipality, protecting natural sites of ecological relevance and recognized tourist importance". (Goiás 1989 ). The Serra Geral APA was created on April 16, 1996, by Decree No. 4.666, with the following objectives: to ensure the protection of the PETeR surroundings and, in particular, the Serra Geral de Goiás hillsides, as well as the springs and river basins, responsible for the natural underground cavities’ formation, and also to control the use and occupation of the soil in the region (Goiás 1996 ). The PETeR region has many visitors, both from geotourism and religious tourism. Every year in August, the so-called Romaria do Bom Jesus da Lapa (Good Jesus of Lapa Pilgrimage) takes place at the Terra Ronca Lapa I cave. During the festival period, the region receives an uncontrolled flow of visitors, which generates significant impacts in the area, such as damage to speleothems, soil compaction, inadequate waste disposal, graffiti, and others. (Zanatto 2019). Methodological procedures The COP method (Vías et al. 2002 ) considers three factors to evaluate the karst aquifers intrinsic vulnerability contamination, these being: the Flow Concentration (C), representing the vulnerability of the aquifer to contamination as a function of infiltration from the karst features; the Protection Layers (O), where it considers the protection provided to the aquifer as a function of physical properties, such as texture, lithology and thickness of the layers above the saturated zone; and the Precipitation (P) that includes both the amount of rainfall in the area, as well as factors that influence the infiltration rate, such as the frequency, temporal distribution, duration and intensity of extreme rainfall events (Fig. 3 ). The C factor (concentration flow) represents the potential for water to pass through protective layers in a concentrated flow through karst features. It considers the surface conditions that control the surface flow to the rapid infiltration zones, such as slope, vegetation, and hydrology. It is evaluated from two scenarios. Scenario 1 is represented by the area where the aquifer recharge is concentrated, and flow passes through the unsaturated zone through the karstic features action. This scenario considers four variables: distance from direct recharge karstic features (dh), distance from streams associated with sinks (ds), slope (s) and vegetation (v). The vulnerability of the aquifer decreases with increasing distance between recharge points and drainage channels. Scenario 2 describes the situation where autogenic recharge occurs, but by means of a concentrated flow, however, from diffuse infiltration. The variables considered are surface features (sf), slope (s) and vegetation (v). The final C-factor map was calculated based on the most vulnerable areas of both scenarios as indicated in Tayer and Velásques (2017). The O factor ( overlying layers ) considers in its analyses the protective layers promoted by the aquifer, these being subdivided into two subfactors, soils [ Os ] and lithology [ Ol ]. The soil subfactor considers parameters related to the physical properties of soils, such as texture, grain size and layer thickness. The lithology sub-factor, on the other hand, analyzes the attenuation capacity of each layer within the unsaturated zone, by means of porosity and hydraulic conductivity characteristics, through the fracturing degree, through the layer thickness, and through the aquifer confinement conditions. The O-factor value and its respective map, is obtained from the sum of the sub-factors [ Os ] and [ Ol ]. The P factor is defined by the amount of rainfall and the factors that influence the infiltration rate, i.e., frequency, temporal distribution, duration, and intensity of extreme rainfall events. These factors help determine the water ability to transport contaminants from the surface to the groundwater. The greater its ability to transport contaminants into the aquifer, the greater the implied vulnerability. This is assessed by two sub-factors, rainfall amount [ Pq ] and temporal distribution of precipitation [ Pi ]. The index of vulnerability to COP aquifer contamination is obtained from the multiplication of the three factors. Data survey For the method application, secondary information was surveyed, available both in bibliographies and in free databases. The pedological and geological data were obtained in Rosa ( 2016 ), at scales of 1:50,000 and 1:250,000, respectively. Rosa ( 2016 ) carried out a refinement of the mapping performed by the RADAMBRASIL Project, at the scale of 1:1,000,000 (Mauro et al. 1982), with the aid of morphometric information (altimetry, slope, slope curvature) from 30-meter Digital Elevation Models and field work. Regarding the texture and layer thickness information, for the work in question, these data were obtained by secondary means, through bibliographies and official databases of pedological characterization, as indicated in the table below. Table 1 Thickness and texture data of soils classified in the Terra Ronca region. Soils Thickness Texture Source Red Argisols (Ultisols) > 1m > 30% Clay Gonçalves et al. 2001 Santos et al., 2018 Red-Yellow Latosol (Oxisol) Ker et al. 1997 Haplic Cambisol 0.5 a 1 m Momoli et al.2022 Litholic Neosols 70% Sand Franco et al. 2020 Quartzarenic Neosols (Entisols) > 1m Momoli et al. 2022 Organized by the authors. Regarding the layer thickness data, the information was obtained from the wells registered in SIAGAS (CPRM 2022), where the static levels values were acquired, seeking to determine the depth of the unsaturated zone of the aquifer in question. A total of 148 wells were identified within a radius of 10 km, which were used in the interpolation of the acquired information for the study area. The rainfall data were obtained from the HidroWeb portal (ANA 2022), where 22 rainfall stations were identified within a radius of up to 10 km. However, only five stations had the data time scale of 30 years, which is the approximate period to determine the regions’ climate o (WMO 2015). The period of the data surveyed was from 1989 to 2019. In relation to karstic features, information regarding the localities of the caves was acquired from the National Center for Cave Research and Conservation (CE-CAV), through the National Registry of Speleological Information database (CANIE), (ICMBIO 2022). Due to the scarcity of information regarding the dolines, the semi-automatic method of identifying karst depressions was used to determine the locality of these features. The method consists in identifying closed depressions from Digital Elevation Models based on procedures in a GIS environment and with the application of morphometric parameters (Carvalho Junior et al. 2014; Silva et al 2022). It is noteworthy that the method use must be accompanied by visual inspection of the polygons generated, discarding the use of false positives. The image used to apply the method was the Copernicus DEM (COP DEM), with spatial resolution of 30 meters. The hydrographic data were obtained from the IBGE portal, which provides a drainage delimited at a scale of 1:100,000 (IBGE 2016). The identification of the sinks was performed from a refinement of these data, with the help of high-resolution images available for free in the Argics 10.3 software. The information regarding land use was acquired in the MapBiomas portal, (https://mapbiomas.org/download), which provides the annual mapping of land cover and use since 1985, from the analysis of Landsat images. The scale of the available products is 1:100,000 and can be viewed at a scale of up to 1:50,000. For the work in question, the mapping of collection 6 for the year 2020 was used (Souza et al. 2020). The weights assigned to each factor can be seen in Table 2 . Table 2 Values used in the variables evaluated to calculate the sub-factors and determining factors of the COP method. The weights were defined according to Viás et al. (2006). Factors Subfactor Variables Value C Surface features (sf) Developed karst Paraopeba Subgroup, Lagoa do Jacaré Formation and Sete Lagoas Formation Terrain 0.75 Sinkholes 0.50 Caves 0.25 Fissure carbonate Serra de Santa Helena Formation Terrain 1 Sinkholes 0.75 Caves 0.75 Non Karstic terrains Other lithologies 1 O Soils (Os) Haplic Gleysol 1 Litholic Neosols 1 Quartzarenic Neosols (Entisols) 2 Haplic Cambisol 4 Red Argisols (Ultisols) 5 Red-Yellow Latosol (Oxisol) 5 Lithology (Ol) Bambuí Group - Paraopeba Subgroup 1 Bambuí Group - Lagoa do Jacaré Formation 1 Bambuí Group - Sete Lagoas Formation 1 Alluvial deposits 10 Ferruginous laterite 40 Urucuia Group 60 Bambuí Group - Serra de Santa Helena Formation 500 Almas-Cavalcante Complex - Orthogneissic-Granitic Unit 1000 São Domingos volcano-sedimentary sequence 1000 P Quantity (Pq) 1633,75–1687,93 (mm/year) 0.4 1333,90–1445,05 (mm/year) 0.3 Temporal distribution (Pi) 16,84 (mm/day) 0.4 Results And Discussion The C factor was calculated based on the information of the sinkhole locations, the presence or not of karstic features, the slope, and the vegetation density. The region of greatest vulnerability related to these factors is predominantly located in the Terra Ronca State Park, where the very high and high classes correspond to about 157.86 km² (3.9%), this being the region with the highest concentration of sinkholes and caves. Tayer and Velásques (2017) point out that areas of this type deserve special attention, since their misuse can cause catastrophic changes in water quality. In relation to slope, more than 65% of the area is represented by flat to gently rolling terrain, indicating a favorable environment for infiltration, depending on soil porosity. The areas with the highest slope are located on the Serra Geral, escarpment representing about 3% of the whole area. For vegetation, the factor analyzed was the vegetational density, being classified according to Viás et al. (2002; 2006), in high and low. The uses classified as having a low density were the Agricultural and Livestock activities and the Countryside Formation, representing 40 and 15%, respectively. The classes considered to have high density were the Savannah and Forest Formations, together representing a total of approximately 45%. The O factor is associated with the upper layers that protect karst aquifers, where lithology, layer thickness and soil texture are considered. Based on the intersection of these data, the areas with very high vulnerability are associated with the carbonate rocks of the Bambuí Group, Paraopeba subgroup and to the Quartzarenic Neosols. These rocks have high solubility characteristics, due to rock dissolution processes in contact with acidified water, and the soils have a sandy texture with low cohesion and low depths, which facilitates infiltration processes and consequently underground contamination (Scopel et al. 2005 ; Goldscheider et al. 2010 ; Hussain et al. 2020). To calculate the precipitation factor (P factor), the data from five rainfall stations in and around the study area were used. The period chosen for analysis was from 1989 to 2019, where among these were used the data from the wettest years, as indicated in Viás et al. (2002;2006). According to the analyses performed, the average precipitation of the wettest years, was 1506.22 mm/year. After the calculation of the Pq (average of rainy years) and Pi (temporal distribution) factors, the final values of the index were determined, which were between the moderate and low vulnerability classes (Fig. 4 ). The weights were assigned to the precipitation factor, based on the data of the five rainfall stations analyzed. The precipitation (Pq) and the temporal distribution (Pi) values were calculated considering the wet years. Regarding the surface characteristics (sf), according to the methodology, the weights were assigned considering the lithological differences. They were considered as being a karst that developed the geological formations of the Paraopeba Subgroup, which are part of the Lagoa do Jacaré Formation and the Sete Lagoas Formation. It is composed of sequences of pelito-carbonate rocks of Neoproterozoic age (750 − 600 Ma). The Serra de Santa Helena Formation rocks, which consisted of a predominantly pelitic sedimentary succession, composed of siltstones, shales, argillites, and marls intercalated with limestones, were considered to have carbonate fissures. (Mauro et al. 1982; Iglesias and Uhlein 2009 ; Chiodi Filho et al. 2003 ). The other lithologies were classified as non-karstic. For the soils subfactor (Os), the weights were attributed considering the secondary information of texture and layer thickness. Considering the pedological diversities that can vary according to the landscape under study, for the research in question, the Gleissols and Neosols (Litholic and Quartz) were considered the soils with the highest probability of underground contamination. This is due to the low thickness of their layers, which can be considered young soils, with the presence of a sandier texture. (Rossi and Queiroz Neto 2002 ; EMBRAPA 2013 ). The three factors required for the generation of the final index, Flux Concentration (Factor C), Shelter Layers (Factor O) and Precipitation (Factor C), presented a clear tendency of spatial distribution in the study area (Fig. 3 ), presenting longitudinally aligned compartments, preferentially. The C factor has the highest values - from very high to high - in the central portion, bordered by a compartment with low values to the east and a very low value to the west, predominantly, and in the western portion, terrain with moderate values predominates. This tendency is related to the distribution of dissolutive features (sinkholes, caves), concentrated predominantly in the central part, over the Sete Lagoas Formation and Paraopeba Subgroup. The O factor presents moderate values in the eastern portion, with occasional occurrences of high values, and in the western portion, low values predominate, although there are portions of moderate and very low values. Factor P, on the other hand, presents low values in the eastern part, and moderate values in the western part. In this distribution it is highlighted that the very high values of the P Factor in the central portion overlap the protected areas, predominantly the Terra Ronca State Park and that the terrains with moderate values of the P Factor coincide with the Terra Ronca Park and the terrains with low values, with the APA (Fig. 2 ). After the individual analysis of the factors, the vulnerability index to groundwater contamination was calculated for the Terra Ronca region, categorized into five (5) classes. These classes were distributed throughout the studied area identifying that the greater vulnerability areas correspond to 33.9 km² (0.9%), followed by the high class with 89.2 km² (2.3%), moderate with 632.2 km² (16.2%), low with 2,095.2 km² (53.7%) and very low with 1,049.4 km² (26,9%). In general, these classes spatial distribution has the same longitudinal organization tendency as the factors that compose the index, especially for the low values in the central portion (Fig. 4 ). On the other hand, the moderate class was restricted to the western portion, while the low and very low classes predominated in the other portions. The predominance of highly vulnerable areas in the central portion is related to the concentration of carbonate rock formations in the region, which led to the appearance of dissolutive features, contributing to the increase of vulnerability in the portion. It is also noteworthy that the soils present in the region, Quartz Neosols and Haplic Cambisols, also have a high vulnerability index, which contributes to the results. Concerning the hydrographic basins, the ones that presented greater vulnerability areas were the São Vicente and São Mateus rivers' basins. This is due to several physical facts, but mainly to the contact between the Sete Lagoas and Serra de Santa Helena Formations, which propitiated the formation of several karstic features, such as caves, dolines and sinkholes, features that facilitate the percolation to the subterranean environment (Parise 2019 ). The other hydrographic basins, São Domingos and Rio de Água Quente, presented themselves as being the areas with the lowest degree of vulnerability in the region under study. This is due to the presence of rocks with a higher degree of resistance, such as the mafic and ultramafic rocks of the São Domingos metavolcano-sedimentary sequence, which present a low probability of infiltration through pores and dissolution conduits, infiltration occurring more effectively in fault and fracture environments (Dávila and Kuyumjian 2005 ). This lithotype also hinders the formation of karstic features, more recurrent in rocks formed by calcium carbonate. Given these conditions, more than 80% of both basins are in the Low and Very Low vulnerability classes. Within these hydrographic basins, the Terra Ronca State Park (PETeR) and the Serra Geral Environmental Protection Area (APA Serra Geral) are located. The Terra Ronca State Park (TR) area was classified as the unit of analysis with the highest vulnerability in the studied region, where 56.5 km² (9.9%) are categorized in the high and very high classes (Fig. 5 ). This area (TR) was also the site of the greatest concentration of caves and sinkholes, features that are highly vulnerable to underground contamination (Faquim et al 2017; Tayer and Velásques, 2017). It is noteworthy that this area, among the other regions, is the most used by tourism, which leads to the generation of various impacts, such as inadequate waste disposal, compaction, and soil erosion on the trails, by the trampling of visitors, which may contribute to the contamination of underground water bodies. The other Protected Area, the APA Serra Geral, presented, on average, a low vulnerability, where about 80% of the area (386.7 km²) was within the low and very low classes. The critical points of this unit are in the southern part of the São Domingos River basin, where carbonate rocks of the Bambuí Group are present, and many caves are concentrated. One of the objectives of the APA creation was to assure the protection of the surroundings of the Terra Ronca State Park and the slopes of the Serra Geral, as well as the springs and river basins, responsible for the formation of natural underground cavities, softening the anthropic advance towards the unit of integral protection (GOIÁS 1996 ). It is noteworthy that in the APA region, the erosive processes are very intense, especially in the escarpment area, being classified as areas highly vulnerable to soil loss, which can also contribute to the pollution and contamination of underground cavities (ROSA 2016 ; Faquim et al 2017). The identified vulnerability values, show similarities with the results presented in Tayer and Velasques (2017), who carried out a study in the APA Carste of Lagoa Santa, using the same method. Tayer and Velasques (2017) found that 10.95% of the Carste of Lagoa Santa area with the highest degree of vulnerability occurs due to the presence of carbonate rocks of the Bambuí Group, associated with the sets of dissolution feature. A situation very similar to our case, where the vulnerability classes Very High and High reached 9.9% for the conservation unit of PETeR, whose purpose is to preserve the areas of natural underground cavities occurrence also in the Bambuí Group carbonate rocks and its surroundings. Regarding the areas classified as moderate to very high, it is noteworthy that in addition to being vulnerable to groundwater contamination, they are also areas with high environmental fragility, due to their lithological characteristics, which favor the formation of unstable structures, which with the presence of human activities, are also considered risk areas (Gutiérrez et al. 2014 ). As the region is a hub of geotourism and religious tourism, the processes of contamination and damage to this environment can occur in an accelerated manner (Zanatto et al. 2019 ). Conclusion According to the analyses carried out, it was identified that the very high vulnerability areas in the Terra Ronca karstic region correspond to 33.89 km² (0.9%), followed by the high class, 89.20 km² (2.3%), moderate, 632.28 km² (16.2%), low, 2095.20 km² (53.7%) and very low, 1049.37 km² (26.9%). The hydrographic basins that indicated the largest vulnerable areas were, the São Vicente River and the São Mateus River, with about 5.0% of the area in the very high and high classes. The results show that the study area is not in an alarming situation, since about 80% of the area presents low and very low vulnerability to karst aquifer contamination. However, the area suffers intense anthropic pressure from its surroundings, as verified by Rosa ( 2016 ). At the same time, the Integral Protection Unit of the Terra Ronca State Park (PETeR) protects only about 56 km² of areas with very high and high vulnerability, corresponding to 9.9% of the PETeR territory, being the analysis unit with the most vulnerable areas of the region. This is due to the presence of several karstic features registered in its limits, which have the greatest weight in the methodology application. It is noteworthy that although these vulnerable areas represent the smallest proportions of the total area and are located in an integral protection unit, the conservation of these environments is not fully guaranteed, as these sites are extremely sensitive due to the intensity of tourism and the influence of agriculture in the Serra Geral region. The anthropic activities presence in the area increases the probability of contamination of these subterranean resources because of the subterranean contamination intrinsic vulnerability. Extrinsic variables can be evaluated in conjunction with the method, such as possible contaminant sources (mining, dumpsites, septic tanks, and others), which can indicate not only the vulnerable areas, but also the contamination risks. It is noteworthy that many karstic features, such as caves, dolines, and sinkholes, have not yet been catalogued, which directly influenced the vulnerability indexes. However, with the results acquired with this study, it is already possible to visualize the region's most critical areas that must be monitored with due attention by the managing bodies, which will help both in the conservation of underground water resources and the speleological heritage. Declarations Acknowledgements We are grateful to the Graduate Geography Program of the Federal University of Goias and Coordination for the Improvement of Higher Education Personnel (CAPES) for funding this research. References ANA (2022) Agência Nacional das Águas. HidroWeb: sistemas de informações hidrológicas (HidroWeb: hydrological information system), Brazilian Water Agency.. Disponível em: https://www.snirh.gov.br/hidroweb/serieshistoricas . Acessado em: 19 de julho de 2022. Aller, L. (1985) DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. Robert S. 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Projeto Rede Integrada de Monitoramento das Águas Subterrâneas: relatório diagnóstico Sistema Aquífero Urucuia Bacia Sedimentar Sanfranciscana. Belo Horizonte: CPRM – Serviço Geológico do Brasil, 2012. (Volume 10). Disponível em: https://rigeo.cprm.gov.br/bitstream/doc/22249/1/aquifero_urucuia_ba.pdf . Acessado: 20 de julho de 2022. Daly D, Dassargue A, Drew D., Dunne S, Goldscheider N, Neale, S, et al (2002) Main concepts of the" European approach" to karst-groundwater-vulnerability assessment and mapping. Hydrogeology Journal, 10 (2), 340–345. https://doi.org/10.1007/s10040-001-0185-1 Dávila C A R, Kuyumjian R M (2005) Mineralizações de ouro do tipo orogênico em arco magmático paleoproterozóico, borda oeste do Cráton São Francisco, regiões de São Domingos (GO) e Correntina (BA). Revista Brasileira de Geociências, 35 (2), 187–198. Disponível em: http://bjg.siteoficial.ws/2005/n.2/4.pdf . Acessado: 09 de setembro de 2022. Doerfliger N, Zwhalen F (1997) EPIK: Cartographic Method for Assessing the Vulnerability of Karst Aquifers for the purpose of Delineating Protection Zones. In U: Proceedings of the 1997 Karst and Cave Management Symposium 13th National Cave Management Symposium, Bellingham, Washington and Chilliwack and Vancouver Island, BC, Canada . Disponível em: https://digitalcommons.usf.edu/cgi/viewcontent.cgi?article=1097 &context=kip_talks#page=68 Acessado: 09 de setembro de 2022. EMBRAPA (2013) Sistema brasileiro de classificação de solos. Centro Nacional de Pesquisa de Solos: Rio de Janeiro , 3 . Disponível em: http://livimagens.sct.embrapa.br/amostras/00053080.pdf Acessado: 09 de setembro de 2022. Faquim, A C D S, Zancopé M H D C, Cherem L F S (2017) Potencial de transferência de sedimentos das bacias contribuintes do sistema cárstico Terra Ronca. doi: 10.5216/bgg.v37i3.50765 FELFILI J M, SOUSA-SILVA J C, Scariot A. (2005) Biodiversidade, ecologia e conservação do Cerrado: avanços no conhecimento. Cerrado: ecologia, biodiversidade e conservação, 25–44. Franco A C S, Ferreira F A O, Souza J D, Mateus N B, Souza J C (2020) Caracterização física simplificada dos solos que margeiam a rodovia GO-070, entre as cidades de Goiás e Itaberaí (GOIÁS). Revista Geoaraguaia, 10 , 155–170. Recuperado de https://periodicoscientificos.ufmt.br/ojs/index.php/geo/article/view/9762 . Goiás (1989) Lei n. 10.879, de 07 de julho de 1989. Cria o Parque Estadual de Terra Ronca. Disponível em: http://www.gabinetecivil.go.gov.br/pagina_leis.php?id=5399 . Acessado: 18 de julho de 2022. Goiás (1996) Decreto n.º 4.666, de 16 de abril de 1996. Declara como Área de Proteção Ambiental, nos Municípios de São Domingos e Guarani de Goiás, região que delimita e dá outras providências. Disponível em: https://www.meioambiente.go.gov.br/files/Unidades_Conservacao/Atos_Criacao/APA_Serra_Geral_4666.pdf . Acessado: 18 de julho de 2022. 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Waste Management, 28, S33-S39. https://doi.org/10.1016/j.wasman.2008.03.024 . Ribeiro D D M, Nascimento S A M, Garcia A J V (2016) Vulnerabilidade dos aquíferos cársticos Sapucari e Maruim, bacia sedimentar de Sergipe-Alagoas. Águas Subterrâneas, 30(3), 375–393. https://doi.org/10.14295/ras.v30i3.28634 . Rosa L E (2016) Interfaces entre unidades de conservação e bacias hidrográficas na região de Terra Ronca (Interfaces between conservation units and river basins in the region of Terra Ronca) (Master Degree Dissertation) The Federal University of Goiás/UFG. Disponível em : https://repositorio.bc.ufg.br/tede/handle/tede/6641 . Acessado em: 12 de setembro de 2022. Rossi M, Queiroz Neto J P (2002). Evolução de espodossolo ferrocárbico em gleissolo háplico no planalto da Serra do Mar, rio Guaratuba (SP). Revista brasileira de ciência do solo, 26, 407–415. https://doi.org/10.1590/S0100-06832002000200014 . Rosenberger M, Varnier C, Iritan M A, Ferreira L M R, ODA G H, Viotti M (2013) Vulnerabilidade natural à contaminação do Sistema Aquífero Bauru na área urbana do município de Bauru (SP). Revista do Instituto Geológico, 34(2), 51–57. doi: 10.5935/0100-929X.20130009 . Santos H G, Jacomine P K T, Anjos L H C, Oliveira V A et al (2018) Sistema brasileiro de classificação de solos . 5 ed. Brasilia: EMBRAPA, 2018. 356 p. Disponível em: https://www.infoteca.cnptia.embrapa.br/handle/doc/1094003 . Acessado em: 12 de setembro de 2022. Silva G B, Cherem L F S, Amaral A K N (2022) O Mapeamento de dolinas utilizando Modelo Digital de Elevação na Borda Ocidental do Chapadão Central–Oeste da Bahia, Brasil. Ateliê Geográfico, 16 (1), 204–216. doi: 10.5216/ag.v16i1.71903 . Scopel I, Peixinho D M, Souza M S, Fátima Mariano Z, Assunção H F (2005) Formação de areais e perspectivas de uso e manejo de Neossolos Quartzarênicos em Serranópolis (GO). Boletim Goiano de Geografia, 25(1–2), 12–27. SNUC (2000) Sistema Nacional de Unidades de Conservação; Lei 9.985 de 18 de julho de 2000; Ministério do Meio Ambiente. Soares M C C (Coord.), Bensusan N, Neto P S F Entorno de Unidades de Conservação: Estudo de Experiências com UCs de Proteção Integral. Disponível em: http://www.planalto.gov.br/ccivil_03/leis/l9985 .htm Acessado em: 12 de setembro de 2022. Souza Jr C M, Shimbo J Z, Rosa M R, Parente L L, et al (2020) Reconstructing three decades of land use and land cover changes in brazilian biomes with landsat archive and earth engine. Remote Sensing, 12(17), 2735. https://doi.org/10.3390/rs12172735 . Pamardhi-Utomo R. (2020) A strategy for the sustainable development of the karst area in Wonogiri. In IOP Conference Series: Earth and Environmental Science (Vol. 447, No. 1, p. 012057). IOP Publishing. doi: 10.1088/1755-1315/447/1/012057 . Stempvoort D V, Ewert L, Wassenaar L (1993) Aquifer vulnerability index: a GIS-compatible method for groundwater vulnerability mapping. Canadian Water Resources Journal, 18(1), 25–37. https://doi.org/10.4296/cwrj1801025 . Tayer T C, Velásques L N M (2017) Assessment of intrinsic vulnerability to the contamination of karst aquifer using the COP method in the Carste Lagoa Santa Environmental Protection Unit, Brazil. Environmental Earth Sciences, 76 (13), 1–13. https://doi.org/10.1007/s12665-017-6760-0 . Trindade H D G (2016) Populações tradicionais e conflitos socioambientais no cerrado: o caso do complexo de unidades de conservação de Terra Ronca - GO. (Master Degree Dissertation). UnB. http://dx.doi.org/10.26512/2016.03.D.23171 . Vías J M, Andreo B, Perles M, Carrasco F, Vadillo I, Jiménez P (2002) Preliminary proposal of a method for vulnerability mapping in carbonate aquifers. En: CARRASCO, F. DURÁN, J. J. y ANDREO, B. (Eds.), Karst and Environment. 2002, p.75–83. Vías J M, Andreo B, Perles M J, Carrasco F, Vadillo I, Jiménez P (2006) Proposed method for groundwater vulnerability mapping in carbonate (karstic) aquifers: the COP method. Hydrogeology Journal, 14(6), 912–925. https://doi.org/10.1007/s10040-006-0023-6 . WMO (2015) World Meterological Organization. New Two-Tier approach on “climate normals”. Disponível em: https://public.wmo.int/en/media/news/new-two-tier-approach-%E2%80%9Cclimate-normals%E2%80%9D# :~:text=Climate%20normals%20are%20presently%20updated,climate%20much%20faster%20than%20before. Acessado em: 19 de julho de 2022. Zanatto V, Steinke V, Vieira A (2019) Impactos do geoturismo na caverna Terra Ronca, Goiás, Brasil. Revista de Geografia e Ordenamento do Território (GOT), n.º 16 (março). Centro de Estudos de Geografia e Ordenamento do Território, p. 391–414, dx.doi.org/10.17127/got/2019.16.017 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2114089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":140736098,"identity":"12cb6ce6-c63a-4b50-b8bc-049d8a64bc2d","order_by":0,"name":"Ana Karolyna Nunes Amaral","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYLCDDwwVQJKZuYFoHYwzGM6AtDCSooWxDUzj18I/u/kBc2HbHTn5Bh7Dxq/zaqP524FaflRsw6lF4s4xA+aZbc+MDQ7wGDbLbjueO+MwYwNjz5nbuK25kWDAzNt2OHEDA4/5Y8ltx3IbgFqYGdtwa5G/kf4BpKV+PtBhzZJzjuXOJ6TF4EYO2JYEBqDDGj821ORuIKTF8EZOwWGec4cNNxxmK2xmOHYgdyNQy0F8fpG7kb7xMU/ZYXn59uaNjT9q6nLnnT988MGPCjzeB4IDYJIZiHgYDiOJEAMYfzDUEa14FIyCUTAKRg4AAPs0XQe2M/8gAAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of Goiás (UFG)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Karolyna Nunes","lastName":"Amaral","suffix":""},{"id":140736099,"identity":"18b3085e-eea3-4ffe-b0f3-5101b66b784b","order_by":1,"name":"Gisele Bispo Silva","email":"","orcid":"","institution":"Federal University of Goiás (UFG)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gisele","middleName":"Bispo","lastName":"Silva","suffix":""},{"id":140736100,"identity":"bf84a36f-e877-48ec-99c1-2c53cda17286","order_by":2,"name":"Lucas Espíndola Rosa","email":"","orcid":"","institution":"Federal University of Goiás (UFG)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"Espíndola","lastName":"Rosa","suffix":""},{"id":140736101,"identity":"cff82021-2c0c-4d00-b1bb-4afdeb82d28c","order_by":3,"name":"Luis Felipe Soares Cherem","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Felipe Soares","lastName":"Cherem","suffix":""},{"id":140736102,"identity":"d76abdf0-ed9f-480e-870e-0efd0d4abbec","order_by":4,"name":"Renata Santos Momoli","email":"","orcid":"","institution":"Federal University of Goiás (UFG)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Renata","middleName":"Santos","lastName":"Momoli","suffix":""},{"id":140736103,"identity":"1f4881a0-0154-47bf-aaef-76fc71e4be32","order_by":5,"name":"Márcio Henrique de Campos Zancopé","email":"","orcid":"","institution":"Federal University of Goiás (UFG)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Márcio","middleName":"Henrique de Campos","lastName":"Zancopé","suffix":""}],"badges":[],"createdAt":"2022-09-28 20:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2114089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2114089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27376852,"identity":"b5edaec0-01fc-4793-9dba-413290a96dbc","added_by":"auto","created_at":"2022-10-05 14:52:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":991513,"visible":true,"origin":"","legend":"\u003cp\u003eThe Terra Ronca karstic region geology map.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2114089/v1/bf0d2ab1cb7f7c0708b36a9c.jpg"},{"id":27375910,"identity":"584fd5f7-81f5-47f5-9d03-ee37dc8da068","added_by":"auto","created_at":"2022-10-05 14:42:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":969038,"visible":true,"origin":"","legend":"\u003cp\u003eThe Terra Ronca karstic region soil map.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2114089/v1/cbb26057a3e5b0baaa4e13b1.jpg"},{"id":27376853,"identity":"456b9d60-56c3-4cf4-b38d-15b763930edb","added_by":"auto","created_at":"2022-10-05 14:52:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133300,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral application flowchart for the intrinsic vulnerability assessment method of karst aquifers - COP. Source: modified from Vías et al.\u003cem\u003e \u003c/em\u003e(2006).\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-2114089/v1/bc0f30a82222baf8e5d7f1ad.png"},{"id":27375913,"identity":"558cefac-3146-400f-9136-aad4612de9bc","added_by":"auto","created_at":"2022-10-05 14:42:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":736094,"visible":true,"origin":"","legend":"\u003cp\u003eFactors C (top left), O (top right) and P (bottom left) considered for calculating the COP index.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2114089/v1/804d9c0d517ceaf2351bf4a5.jpg"},{"id":27376409,"identity":"a922f606-51d4-49b2-b9c7-5a599778af58","added_by":"auto","created_at":"2022-10-05 14:47:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":698361,"visible":true,"origin":"","legend":"\u003cp\u003eIntrinsic vulnerability map using the COP method in the Terra Ronca region. A) Vulnerability classes near Terra Ronca cave.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2114089/v1/e1decd3e3ca6b3932f91ce64.jpg"},{"id":27375909,"identity":"7a6bd700-b7ba-4d36-a679-e3a44e3e649a","added_by":"auto","created_at":"2022-10-05 14:42:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41091,"visible":true,"origin":"","legend":"\u003cp\u003eRelative distribution of underground contamination vulnerability among the region units of analysis, where the colors indicate: red, very high vulnerability; orange, high; yellow, moderate; light green, low; and dark green, very low. In 'total' is the relative distribution of vulnerability to contamination in the whole study area; 'TR' is the relative distribution in the territory of the PETeR; 'APA' is the distribution in the Serra Geral Area of Environmental Protection for Sustainable Use; 'SD' is the relative distribution in the São Domingos River basin; 'SV', in the São Vicente River basin; 'SM' in the São Mateus river basin; and 'AQ', in the Água Quente river basin.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2114089/v1/b99116d9e01afb8c0c3026ae.png"},{"id":31274708,"identity":"dce3fbf3-7d19-456a-a75e-90cb3d8910ba","added_by":"auto","created_at":"2023-01-08 16:29:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1383185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2114089/v1/55ad6894-eb72-4254-9921-cee83b1f4596.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eContamination Vulnerability Assessment of the Bambuí Karst Aquifer, in the Terra Ronca Region - Goiás, Brazil\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAquifers are all rock bodies or formations capable of storing and transmitting water. This capacity is specific to each lithotype, that is, the capacity to store (porosity) and transmit water (permeability), the geological factors result in interaction with the rock body since its formation. Thus, each lithological type brings its own hydrogeological characteristics when it is formed, and in turn reacts in its own way to the mitigating factors (Guerra \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKarst aquifers result from the solubilizing action of water on carbonate rocks. In the karstification process there is a basic mechanism that is the dissolution by water of a carbonate rock (soluble). Particular to karst are the entries of surface water in localized conduits, called sinkholes. This localized mode of seepage is due to the great spatial permeability variability and seepage capacity, which is much higher in karstic environments than in other permeable environments (Santos et al. 2010).\u003c/p\u003e \u003cp\u003eThus, karst areas are of great economic and hydrogeological interest because of their significant groundwater reserves and mineral occurrence, which is used both in construction and agriculture as well as in tourism (Soedwiwahjono and Pamardhi-utomo \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They have a naturally high degree of vulnerability, and depending on the activities performed on this environment, groundwater is increasingly prone to pollution, given the supply of populations, irrigation, and industry (Ribeiro et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sense, studies focused on the karst aquifers vulnerability theme are of fundamental importance, as they help in activities that aim to protect the recharge areas and, consequently, preserve the good quality of the waters, in face of the technical and financial limitations resulting from the possible contaminations remediation. Furthermore, they allow the understanding of the different degrees of vulnerability of aquifers, being used as a management tool for local and regional development (Santos et al., 2010; Ribeiro et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe vulnerability concept can be understood, according to Foster and Hirata (1988), as the set of intrinsic characteristics of the strata between the saturated zone and the soil surface, which determines their susceptibility to suffer the adverse contaminant load effects. Over the years, several methods for assessing the aquifers contamination vulnerability have emerged, among these, the most widely used were: DRAS-TIC (Aller et al. 1985); AVI (Stempvoort et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1993\u003c/span\u003e); GOD (Foster and Hirata, 1988); EPIK (Doerfliger and Zwahlen, 1997); COP (V\u0026iacute;as et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe COP method was developed by V\u0026iacute;as et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and is based on the premises of the European COST Action 620 on the assessment of aquifer contamination vulnerability (Daly et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). It was initially applied to carbonate aquifers in southern Spain, Sierra do Libar and Torremolinos, where it showed good results in comparison with other methods (AVI, GOD and DRASTIC) (V\u0026iacute;as et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The main advantages in relation to other methods, is that it can be applied in different climatic conditions, and in different types of carbonate aquifers, as well as in environments that are not fully karstic, considering different weights in the methodology application for these regions.\u003c/p\u003e \u003cp\u003eThis method has already been applied in several locations, such as the Lez karst system in France, the Kaibab Plateau in Arizona, USA, and the Lagoa Santa karst in Minas Gerais, Brazil. In all cases, satisfactory analyses were presented in the indication of areas vulnerable to underground contamination, demonstrating the possibility of application in different climatic regions (Mar\u0026iacute;n et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tayer and Vel\u0026aacute;sques, 2017; Jones et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Terra Ronca State Park (PETeR) region and surroundings is located over an extensive karst area and is known for having one of the largest concentrations of caves in Goi\u0026aacute;s State and in Brazil, being highly exploited by tourism. These environments are extremely fragile, and need an active management, so as to mitigate possible environmental damage (Parise et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), since it is under intense anthropic pression by agricultural activities that are developed in its surroundings, as well as by the morphodynamics of the Serra Geral zone, capable of providing large amounts of sediments to the karstic conduits (Faquim et al. 2017; Aleixo et al. 2019).\u003c/p\u003e \u003cp\u003eRosenberger (2013) points out that the delimitation of naturally vulnerable areas is a widely used technique for the groundwater protection, aiming to reconcile anthropic activities and the terrain capacity to support them environmentally. Considering the extent of carbonate rocks in the study area and the lack of studies on the aquifers vulnerability in the region, the objective of this paper is to assess the intrinsic vulnerability of the Bambu\u0026iacute; Aquifer to groundwater contamination in the Terra Ronca region, Goi\u0026aacute;s State, through the COP method.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eA The study area is located in the headwaters of the right-hand tributaries of the Paran\u0026atilde; River, a tributary of the eastern portion of the Tocantins River Basin, specifically near the Tocantins/S\u0026atilde;o Francisco divider, in Central Brazil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This area covers part of the micro-region of the V\u0026atilde;o do Paran\u0026atilde;, known as one of the three main centers of the Cerrado biome endemism (Felfili et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), comprising the watersheds of the S\u0026atilde;o Domingos, S\u0026atilde;o Vicente, S\u0026atilde;o Mateus, and \u0026Aacute;gua Quente rivers.\u003c/p\u003e \u003cp\u003eThe region is located on the western border of the S\u0026atilde;o Francisco Craton, where Cretaceous sedimentary rocks of the Urucuia Group, Paleozoic meta-sedimentary rocks of the Santa F\u0026eacute; Group, among others emerge, (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), mainly Neoproterozoic meta-sedimentary and meta-bearing rocks of the Bambu\u0026iacute; Group. The Bambu\u0026iacute; Group is formed by two rocky successions, the first, basal marine, is composed of the Sete Lagoas, Serra de Santa Helena, Lagoa do Jacar\u0026eacute; and Serra da Saudade formations, which compose the Paraopeba Subgroup. The second succession is the marine-continental one represented by the Tr\u0026ecirc;s Marias Formation, predominantly psammite. The area is known for the presence of karst geological features such as dolines, lapis, sinkholes, and caves (Mauro et al. 1982; Iglesias and Uhlein, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jansen et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the eastern part of the study area, the presence of the Serra Geral de Goi\u0026aacute;s escarpments stands out. These form the western edge of the Tocantins/S\u0026atilde;o Francisco plateaus, marking the limit between the states of Goi\u0026aacute;s and Bahia. This eastern region is marked by the presence of sandstones of the Urucuia Group, which are aquifers that serve as springs for some of the main tributaries of the Tocantins River Basin eastern margin (CPRM \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Faquim et al. 2017).\u003c/p\u003e \u003cp\u003eThe region climate is classified as tropical sub-humid (Aw), according to the K\u0026ouml;ppen climate classification, marked by two well-defined seasons. The use and vegetation cover of the region, according to the mapping of land use performed by the MapBiomas project in 2020, are dominated by the Savanna Formation (41.14%), pasture (28.04%), Campestre Formation (15.96%) and agriculture (7.46%), (Souza et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The soils with greater representation in the study area were Quartz Neosols, with a coverage of 1728.71 km\u0026sup2;, corresponding to 43.21% of the area, followed by the Cambisolos H\u0026aacute;plicos, 27.81%, and Red Argissolos, 14.95% (Mauro et al. 1982; Rosa \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWithin the study area there are two conservation units, one of full protection, the Terra Ronca State Park (PETeR) and another of sustainable use, the Serra Geral Environmental Protection Area (APA Serra Geral). The PETeR was created on July 7, 1989, by Law N\u0026ordm; 10.879, with its main objective being \"to preserve the flora, fauna, springs and, in particular, the areas of underground natural cavities occurrence and their surroundings, found in the S\u0026atilde;o Domingos Municipality, protecting natural sites of ecological relevance and recognized tourist importance\". (Goi\u0026aacute;s \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). The Serra Geral APA was created on April 16, 1996, by Decree No. 4.666, with the following objectives: to ensure the protection of the PETeR surroundings and, in particular, the Serra Geral de Goi\u0026aacute;s hillsides, as well as the springs and river basins, responsible for the natural underground cavities\u0026rsquo; formation, and also to control the use and occupation of the soil in the region (Goi\u0026aacute;s \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PETeR region has many visitors, both from geotourism and religious tourism. Every year in August, the so-called Romaria do Bom Jesus da Lapa (Good Jesus of Lapa Pilgrimage) takes place at the Terra Ronca Lapa I cave. During the festival period, the region receives an uncontrolled flow of visitors, which generates significant impacts in the area, such as damage to speleothems, soil compaction, inadequate waste disposal, graffiti, and others. (Zanatto 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMethodological procedures\u003c/h2\u003e \u003cp\u003eThe COP method (V\u0026iacute;as et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) considers three factors to evaluate the karst aquifers intrinsic vulnerability contamination, these being: the Flow Concentration (C), representing the vulnerability of the aquifer to contamination as a function of infiltration from the karst features; the Protection Layers (O), where it considers the protection provided to the aquifer as a function of physical properties, such as texture, lithology and thickness of the layers above the saturated zone; and the Precipitation (P) that includes both the amount of rainfall in the area, as well as factors that influence the infiltration rate, such as the frequency, temporal distribution, duration and intensity of extreme rainfall events (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe C factor (concentration flow) represents the potential for water to pass through protective layers in a concentrated flow through karst features. It considers the surface conditions that control the surface flow to the rapid infiltration zones, such as slope, vegetation, and hydrology. It is evaluated from two scenarios.\u003c/p\u003e \u003cp\u003eScenario 1 is represented by the area where the aquifer recharge is concentrated, and flow passes through the unsaturated zone through the karstic features action. This scenario considers four variables: distance from direct recharge karstic features (dh), distance from streams associated with sinks (ds), slope (s) and vegetation (v). The vulnerability of the aquifer decreases with increasing distance between recharge points and drainage channels. Scenario 2 describes the situation where autogenic recharge occurs, but by means of a concentrated flow, however, from diffuse infiltration. The variables considered are surface features (sf), slope (s) and vegetation (v). The final C-factor map was calculated based on the most vulnerable areas of both scenarios as indicated in Tayer and Vel\u0026aacute;sques (2017).\u003c/p\u003e \u003cp\u003eThe O factor (\u003cem\u003eoverlying layers\u003c/em\u003e) considers in its analyses the protective layers promoted by the aquifer, these being subdivided into two subfactors, soils [\u003cem\u003eOs\u003c/em\u003e] and lithology [\u003cem\u003eOl\u003c/em\u003e]. The soil subfactor considers parameters related to the physical properties of soils, such as texture, grain size and layer thickness. The lithology sub-factor, on the other hand, analyzes the attenuation capacity of each layer within the unsaturated zone, by means of porosity and hydraulic conductivity characteristics, through the fracturing degree, through the layer thickness, and through the aquifer confinement conditions. The O-factor value and its respective map, is obtained from the sum of the sub-factors [\u003cem\u003eOs\u003c/em\u003e] and [\u003cem\u003eOl\u003c/em\u003e].\u003c/p\u003e \u003cp\u003eThe P factor is defined by the amount of rainfall and the factors that influence the infiltration rate, i.e., frequency, temporal distribution, duration, and intensity of extreme rainfall events. These factors help determine the water ability to transport contaminants from the surface to the groundwater. The greater its ability to transport contaminants into the aquifer, the greater the implied vulnerability. This is assessed by two sub-factors, rainfall amount [\u003cem\u003ePq\u003c/em\u003e] and temporal distribution of precipitation [\u003cem\u003ePi\u003c/em\u003e]. The index of vulnerability to COP aquifer contamination is obtained from the multiplication of the three factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData survey\u003c/h2\u003e \u003cp\u003eFor the method application, secondary information was surveyed, available both in bibliographies and in free databases. The pedological and geological data were obtained in Rosa (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), at scales of 1:50,000 and 1:250,000, respectively. Rosa (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) carried out a refinement of the mapping performed by the RADAMBRASIL Project, at the scale of 1:1,000,000 (Mauro et al. 1982), with the aid of morphometric information (altimetry, slope, slope curvature) from 30-meter Digital Elevation Models and field work. Regarding the texture and layer thickness information, for the work in question, these data were obtained by secondary means, through bibliographies and official databases of pedological characterization, as indicated in the table below.\u003c/p\u003e \n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThickness and texture data of soils classified in the Terra Ronca region.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoils\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThickness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTexture\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRed\u0026nbsp;Argisols (Ultisols)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;30% Clay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGon\u0026ccedil;alves et al. \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eSantos et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRed-Yellow Latosol (Oxisol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKer et al. 1997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaplic\u0026nbsp;Cambisol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 a 1 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMomoli et al.2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLitholic\u0026nbsp;Neosols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaplic Gleysol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 a 1 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;70% Sand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFranco et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartzarenic Neosols\u0026nbsp;(Entisols)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMomoli et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eOrganized by the authors.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the layer thickness data, the information was obtained from the wells registered in SIAGAS (CPRM 2022), where the static levels values were acquired, seeking to determine the depth of the unsaturated zone of the aquifer in question. A total of 148 wells were identified within a radius of 10 km, which were used in the interpolation of the acquired information for the study area.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rainfall data were obtained from the HidroWeb portal (ANA 2022), where 22 rainfall stations were identified within a radius of up to 10 km. However, only five stations had the data time scale of 30 years, which is the approximate period to determine the regions\u0026rsquo; climate o (WMO 2015). The period of the data surveyed was from 1989 to 2019. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn relation to karstic features, information regarding the localities of the caves was acquired from the National Center for Cave Research and Conservation (CE-CAV), through the National Registry of Speleological Information database (CANIE), (ICMBIO 2022). Due to the scarcity of information regarding the dolines, the semi-automatic method of identifying karst depressions was used to determine the locality of these features. The method consists in identifying closed depressions from Digital Elevation Models based on procedures in a GIS environment and with the application of morphometric parameters (Carvalho Junior et al. 2014; Silva et al 2022). It is noteworthy that the method use must be accompanied by visual inspection of the polygons generated, discarding the use of false positives. The image used to apply the method was the Copernicus DEM (COP DEM), with spatial resolution of 30 meters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe hydrographic data were obtained from the IBGE portal, which provides a drainage delimited at a scale of 1:100,000 (IBGE 2016). The identification of the sinks was performed from a refinement of these data, with the help of high-resolution images available for free in the Argics 10.3 software. The information regarding land use was acquired in the MapBiomas portal, (https://mapbiomas.org/download), which provides the annual mapping of land cover and use since 1985, from the analysis of Landsat images. The scale of the available products is 1:100,000 and can be viewed at a scale of up to 1:50,000. For the work in question, the mapping of collection 6 for the year 2020 was used (Souza et al. 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe weights assigned to each factor can be seen in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues used in the variables evaluated to calculate the sub-factors and determining factors of the COP method. The weights were defined according to Vi\u0026aacute;s et al. (2006).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubfactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eSurface features (sf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDeveloped karst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eParaopeba Subgroup, Lagoa do Jacar\u0026eacute; Formation and Sete Lagoas Formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSinkholes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eFissure carbonate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSerra de Santa Helena Formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSinkholes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Karstic terrains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOther lithologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"15\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eSoils (Os)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHaplic Gleysol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLitholic\u0026nbsp;Neosols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eQuartzarenic Neosols\u0026nbsp;(Entisols)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHaplic\u0026nbsp;Cambisol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRed\u0026nbsp;Argisols (Ultisols)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRed-Yellow Latosol (Oxisol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eLithology (Ol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBambu\u0026iacute; Group - Paraopeba Subgroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBambu\u0026iacute; Group - Lagoa do Jacar\u0026eacute; Formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBambu\u0026iacute; Group - Sete Lagoas Formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAlluvial deposits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFerruginous laterite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUrucuia Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBambu\u0026iacute; Group - Serra de Santa Helena Formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAlmas-Cavalcante Complex - Orthogneissic-Granitic Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eS\u0026atilde;o Domingos\u0026nbsp;volcano-sedimentary sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eQuantity (Pq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1633,75\u0026ndash;1687,93 (mm/year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1333,90\u0026ndash;1445,05 (mm/year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporal distribution (Pi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e16,84 (mm/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results And Discussion","content":"\u003cp\u003eThe C factor was calculated based on the information of the sinkhole locations, the presence or not of karstic features, the slope, and the vegetation density. The region of greatest vulnerability related to these factors is predominantly located in the Terra Ronca State Park, where the very high and high classes correspond to about 157.86 km\u0026sup2; (3.9%), this being the region with the highest concentration of sinkholes and caves. Tayer and Vel\u0026aacute;sques (2017) point out that areas of this type deserve special attention, since their misuse can cause catastrophic changes in water quality.\u003c/p\u003e \u003cp\u003eIn relation to slope, more than 65% of the area is represented by flat to gently rolling terrain, indicating a favorable environment for infiltration, depending on soil porosity. The areas with the highest slope are located on the Serra Geral, escarpment representing about 3% of the whole area. For vegetation, the factor analyzed was the vegetational density, being classified according to Vi\u0026aacute;s et al. (2002; 2006), in high and low. The uses classified as having a low density were the Agricultural and Livestock activities and the Countryside Formation, representing 40 and 15%, respectively. The classes considered to have high density were the Savannah and Forest Formations, together representing a total of approximately 45%.\u003c/p\u003e \u003cp\u003eThe O factor is associated with the upper layers that protect karst aquifers, where lithology, layer thickness and soil texture are considered. Based on the intersection of these data, the areas with very high vulnerability are associated with the carbonate rocks of the Bambu\u0026iacute; Group, Paraopeba subgroup and to the Quartzarenic Neosols. These rocks have high solubility characteristics, due to rock dissolution processes in contact with acidified water, and the soils have a sandy texture with low cohesion and low depths, which facilitates infiltration processes and consequently underground contamination (Scopel et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Goldscheider et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hussain et al. 2020).\u003c/p\u003e \u003cp\u003eTo calculate the precipitation factor (P factor), the data from five rainfall stations in and around the study area were used. The period chosen for analysis was from 1989 to 2019, where among these were used the data from the wettest years, as indicated in Vi\u0026aacute;s et al. (2002;2006). According to the analyses performed, the average precipitation of the wettest years, was 1506.22 mm/year. After the calculation of the Pq (average of rainy years) and Pi (temporal distribution) factors, the final values of the index were determined, which were between the moderate and low vulnerability classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The weights were assigned to the precipitation factor, based on the data of the five rainfall stations analyzed. The precipitation (Pq) and the temporal distribution (Pi) values were calculated considering the wet years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the surface characteristics (sf), according to the methodology, the weights were assigned considering the lithological differences. They were considered as being a karst that developed the geological formations of the Paraopeba Subgroup, which are part of the Lagoa do Jacar\u0026eacute; Formation and the Sete Lagoas Formation. It is composed of sequences of pelito-carbonate rocks of Neoproterozoic age (750\u0026thinsp;\u0026minus;\u0026thinsp;600 Ma). The Serra de Santa Helena Formation rocks, which consisted of a predominantly pelitic sedimentary succession, composed of siltstones, shales, argillites, and marls intercalated with limestones, were considered to have carbonate fissures. (Mauro et al. 1982; Iglesias and Uhlein \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chiodi Filho et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The other lithologies were classified as non-karstic.\u003c/p\u003e \u003cp\u003eFor the soils subfactor (Os), the weights were attributed considering the secondary information of texture and layer thickness. Considering the pedological diversities that can vary according to the landscape under study, for the research in question, the Gleissols and Neosols (Litholic and Quartz) were considered the soils with the highest probability of underground contamination. This is due to the low thickness of their layers, which can be considered young soils, with the presence of a sandier texture. (Rossi and Queiroz Neto \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; EMBRAPA \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe three factors required for the generation of the final index, Flux Concentration (Factor C), Shelter Layers (Factor O) and Precipitation (Factor C), presented a clear tendency of spatial distribution in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), presenting longitudinally aligned compartments, preferentially. The C factor has the highest values - from very high to high - in the central portion, bordered by a compartment with low values to the east and a very low value to the west, predominantly, and in the western portion, terrain with moderate values predominates. This tendency is related to the distribution of dissolutive features (sinkholes, caves), concentrated predominantly in the central part, over the Sete Lagoas Formation and Paraopeba Subgroup.\u003c/p\u003e \u003cp\u003eThe O factor presents moderate values in the eastern portion, with occasional occurrences of high values, and in the western portion, low values predominate, although there are portions of moderate and very low values. Factor P, on the other hand, presents low values in the eastern part, and moderate values in the western part. In this distribution it is highlighted that the very high values of the P Factor in the central portion overlap the protected areas, predominantly the Terra Ronca State Park and that the terrains with moderate values of the P Factor coincide with the Terra Ronca Park and the terrains with low values, with the APA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter the individual analysis of the factors, the vulnerability index to groundwater contamination was calculated for the Terra Ronca region, categorized into five (5) classes. These classes were distributed throughout the studied area identifying that the greater vulnerability areas correspond to 33.9 km\u0026sup2; (0.9%), followed by the high class with 89.2 km\u0026sup2; (2.3%), moderate with 632.2 km\u0026sup2; (16.2%), low with 2,095.2 km\u0026sup2; (53.7%) and very low with 1,049.4 km\u0026sup2; (26,9%). In general, these classes spatial distribution has the same longitudinal organization tendency as the factors that compose the index, especially for the low values in the central portion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the other hand, the moderate class was restricted to the western portion, while the low and very low classes predominated in the other portions.\u003c/p\u003e \u003cp\u003eThe predominance of highly vulnerable areas in the central portion is related to the concentration of carbonate rock formations in the region, which led to the appearance of dissolutive features, contributing to the increase of vulnerability in the portion. It is also noteworthy that the soils present in the region, Quartz Neosols and Haplic Cambisols, also have a high vulnerability index, which contributes to the results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConcerning the hydrographic basins, the ones that presented greater vulnerability areas were the S\u0026atilde;o Vicente and S\u0026atilde;o Mateus rivers' basins. This is due to several physical facts, but mainly to the contact between the Sete Lagoas and Serra de Santa Helena Formations, which propitiated the formation of several karstic features, such as caves, dolines and sinkholes, features that facilitate the percolation to the subterranean environment (Parise \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe other hydrographic basins, S\u0026atilde;o Domingos and Rio de \u0026Aacute;gua Quente, presented themselves as being the areas with the lowest degree of vulnerability in the region under study. This is due to the presence of rocks with a higher degree of resistance, such as the mafic and ultramafic rocks of the S\u0026atilde;o Domingos metavolcano-sedimentary sequence, which present a low probability of infiltration through pores and dissolution conduits, infiltration occurring more effectively in fault and fracture environments (D\u0026aacute;vila and Kuyumjian \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This lithotype also hinders the formation of karstic features, more recurrent in rocks formed by calcium carbonate. Given these conditions, more than 80% of both basins are in the Low and Very Low vulnerability classes.\u003c/p\u003e \u003cp\u003eWithin these hydrographic basins, the Terra Ronca State Park (PETeR) and the Serra Geral Environmental Protection Area (APA Serra Geral) are located. The Terra Ronca State Park (TR) area was classified as the unit of analysis with the highest vulnerability in the studied region, where 56.5 km\u0026sup2; (9.9%) are categorized in the high and very high classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This area (TR) was also the site of the greatest concentration of caves and sinkholes, features that are highly vulnerable to underground contamination (Faquim et al 2017; Tayer and Vel\u0026aacute;sques, 2017). It is noteworthy that this area, among the other regions, is the most used by tourism, which leads to the generation of various impacts, such as inadequate waste disposal, compaction, and soil erosion on the trails, by the trampling of visitors, which may contribute to the contamination of underground water bodies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe other Protected Area, the APA Serra Geral, presented, on average, a low vulnerability, where about 80% of the area (386.7 km\u0026sup2;) was within the low and very low classes. The critical points of this unit are in the southern part of the S\u0026atilde;o Domingos River basin, where carbonate rocks of the Bambu\u0026iacute; Group are present, and many caves are concentrated. One of the objectives of the APA creation was to assure the protection of the surroundings of the Terra Ronca State Park and the slopes of the Serra Geral, as well as the springs and river basins, responsible for the formation of natural underground cavities, softening the anthropic advance towards the unit of integral protection (GOI\u0026Aacute;S \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). It is noteworthy that in the APA region, the erosive processes are very intense, especially in the escarpment area, being classified as areas highly vulnerable to soil loss, which can also contribute to the pollution and contamination of underground cavities (ROSA \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Faquim et al 2017).\u003c/p\u003e \u003cp\u003eThe identified vulnerability values, show similarities with the results presented in Tayer and Velasques (2017), who carried out a study in the APA Carste of Lagoa Santa, using the same method. Tayer and Velasques (2017) found that 10.95% of the Carste of Lagoa Santa area with the highest degree of vulnerability occurs due to the presence of carbonate rocks of the Bambu\u0026iacute; Group, associated with the sets of dissolution feature. A situation very similar to our case, where the vulnerability classes Very High and High reached 9.9% for the conservation unit of PETeR, whose purpose is to preserve the areas of natural underground cavities occurrence also in the Bambu\u0026iacute; Group carbonate rocks and its surroundings.\u003c/p\u003e \u003cp\u003eRegarding the areas classified as moderate to very high, it is noteworthy that in addition to being vulnerable to groundwater contamination, they are also areas with high environmental fragility, due to their lithological characteristics, which favor the formation of unstable structures, which with the presence of human activities, are also considered risk areas (Guti\u0026eacute;rrez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As the region is a hub of geotourism and religious tourism, the processes of contamination and damage to this environment can occur in an accelerated manner (Zanatto et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to the analyses carried out, it was identified that the very high vulnerability areas in the Terra Ronca karstic region correspond to 33.89 km\u0026sup2; (0.9%), followed by the high class, 89.20 km\u0026sup2; (2.3%), moderate, 632.28 km\u0026sup2; (16.2%), low, 2095.20 km\u0026sup2; (53.7%) and very low, 1049.37 km\u0026sup2; (26.9%). The hydrographic basins that indicated the largest vulnerable areas were, the S\u0026atilde;o Vicente River and the S\u0026atilde;o Mateus River, with about 5.0% of the area in the very high and high classes.\u003c/p\u003e \u003cp\u003eThe results show that the study area is not in an alarming situation, since about 80% of the area presents low and very low vulnerability to karst aquifer contamination. However, the area suffers intense anthropic pressure from its surroundings, as verified by Rosa (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At the same time, the Integral Protection Unit of the Terra Ronca State Park (PETeR) protects only about 56 km\u0026sup2; of areas with very high and high vulnerability, corresponding to 9.9% of the PETeR territory, being the analysis unit with the most vulnerable areas of the region. This is due to the presence of several karstic features registered in its limits, which have the greatest weight in the methodology application.\u003c/p\u003e \u003cp\u003eIt is noteworthy that although these vulnerable areas represent the smallest proportions of the total area and are located in an integral protection unit, the conservation of these environments is not fully guaranteed, as these sites are extremely sensitive due to the intensity of tourism and the influence of agriculture in the Serra Geral region. The anthropic activities presence in the area increases the probability of contamination of these subterranean resources because of the subterranean contamination intrinsic vulnerability. Extrinsic variables can be evaluated in conjunction with the method, such as possible contaminant sources (mining, dumpsites, septic tanks, and others), which can indicate not only the vulnerable areas, but also the contamination risks.\u003c/p\u003e \u003cp\u003eIt is noteworthy that many karstic features, such as caves, dolines, and sinkholes, have not yet been catalogued, which directly influenced the vulnerability indexes. However, with the results acquired with this study, it is already possible to visualize the region's most critical areas that must be monitored with due attention by the managing bodies, which will help both in the conservation of underground water resources and the speleological heritage.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are grateful to the Graduate Geography Program of the Federal University of Goias and Coordination for the Improvement of Higher Education Personnel (CAPES) for funding this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eANA (2022) Ag\u0026ecirc;ncia Nacional das \u0026Aacute;guas. HidroWeb: sistemas de informa\u0026ccedil;\u0026otilde;es hidrol\u0026oacute;gicas (HidroWeb: hydrological information system), Brazilian Water Agency.. 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Acessado em: 19 de julho de 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanatto V, Steinke V, Vieira A (2019) Impactos do geoturismo na caverna Terra Ronca, Goi\u0026aacute;s, Brasil. Revista de Geografia e Ordenamento do Territ\u0026oacute;rio (GOT), n.\u0026ordm; 16 (mar\u0026ccedil;o). Centro de Estudos de Geografia e Ordenamento do Territ\u0026oacute;rio, p.\u0026nbsp;391\u0026ndash;414, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edx.doi.org/10.17127/got/2019.16.017\u003c/span\u003e\u003cspan address=\"10.17127/got/2019.16.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intrinsic Vulnerability, Karst Aquifers, Geoprocessing, Environmental Zoning","lastPublishedDoi":"10.21203/rs.3.rs-2114089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2114089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKarstic environments are characterized as areas of intense rock dissolution, which allows the generation of several typical features, such as sinkholes, resurgences, caves, and dolines. The spatial distribution of these features is of great importance to identify the contamination vulnerability of karst aquifers and serve as a basis for the application of assessment methods. This study aimed to identify and classify the areas vulnerable to underground contamination in the karstic region of the Terra Ronca State Park (PETeR) and its surroundings. The method chosen was the COP, which assesses the physical conditions of the environment (relief, lithology, soils, precipitation, karst features, and vegetation) to determine the areas naturally vulnerable to contamination. The analyses were performed in a GIS environment. The results indicated five categories of areas vulnerable to groundwater contamination: Very High to High (3.14%), Moderate (16.21%), and Low to Very Low (80.62%). The Terra Ronca State Park was the analysis unit with the highest vulnerability in the region (9.91%), due to the concentration of karstic features in the area. The acquired results aim to help in the underground water resources conservation as well as the speleological patrimony.\u003c/p\u003e","manuscriptTitle":"Contamination Vulnerability Assessment of the Bambuí Karst Aquifer, in the Terra Ronca Region - Goiás, Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-05 14:42:12","doi":"10.21203/rs.3.rs-2114089/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cbc0776-eff1-4451-8bf4-734664dacf98","owner":[],"postedDate":"October 5th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-18T00:59:13+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-05 14:42:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2114089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2114089","identity":"rs-2114089","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}