Impacts of rural and urban sources on a tropical semiarid region (Acaraú River, Ceará, Brazil): sedimentary sterols and endocrine-disrupting compounds as anthropogenic molecular markers

Impacts of rural and urban sources on a tropical semiarid region (Acaraú River, Ceará, Brazil): sedimentary sterols and endocrine-disrupting compounds as anthropogenic molecular markers | 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 Impacts of rural and urban sources on a tropical semiarid region (Acaraú River, Ceará, Brazil): sedimentary sterols and endocrine-disrupting compounds as anthropogenic molecular markers Rivelino M. Cavalcante, Antonio D. S. Pereira, Marcielly F. B Lima, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7054890/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Aug, 2025 Read the published version in Environmental Geochemistry and Health → Version 1 posted 10 You are reading this latest preprint version Abstract Semiarid regions are unique, and in Northeast Brazil, estuarine basins are often impacted by human occupation, resulting in the input of chemicals. The objective of this study was to evaluate the contributions of urban and rural activities to the environmental quality of the Acaraú River, using molecular markers in superficial sediments. Concentrations of total sterols and hormones varied between 271.5 to 2525 ng g -1 and 139.8 to 1728 ng g -1 , respectively. Fecal sterol coprostanol ranged from 6 to 124.1 ng g -1 . Concentrations of synthetic hormones were detected at one order of magnitude higher than those of natural hormones, and the diagnostic ratios for sterols, hormones, and coprostanol suggest sewage discharge and fecal contamination in the Aracaú River Basin. Activities such as fish and shrimp farming, which involve the use of drugs for animal handling, may also be relevant sources in the region. Regarding the ecological risks of toxicity, 17α-ethinylestradiol, mestranol, and estrone are compounds of environmental concern in the Acarú River, requiring actions to reduce or eliminate their sources. Fecal sterols endocrine disrupting compounds estrogen hormones sewage pollution source apportionment ecological risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The intense population growth in coastal regions of Brazil has caused strong pressure on the natural balance of these systems. At least 25% of the Brazilian population lives in coastal cities (Szlafsztein and Sterr, 2007 ), which is a consequence of tourism and the economic potential associated with natural coastal areas, making them vulnerable, including estuaries. Anthropic and industrial activities are the main sources of pollutants introduced to the environment by sewage. This has added to the lack of effective wastewater (Sato et al., 2013 ), and the input of sewage is a major concern in coastal management. The presence of sewage in natural waters frequently causes eutrophication, oxygen depletion (Fisher et al., 2006 ), diminished balnealbility, and other problems. Furthermore, some substances present in sewage are difficult to treat, and some are not legally regulated by countries. Persistent Organic Pollutants (POPs), Emergent Organic Contaminants (EOCs), and natural and synthetic organic compounds that are present in rivers or seas may compromise the quality of water and sediments and cause adverse effects on fauna, flora, and human health (Rosenfeld and Feng, 2011 ). These substances can be found as components or active ingredients in many products, such as human and veterinary medicines, industry, personal care products, hormones and steroids, and food additives (Richardson and Ternes, 2011 ; Lapworth et al., 2012 ). Anthropogenic Molecular Markers are natural and anthropogenic compounds whose origin can provide an environmental diagnostic by being persistent and source-specific (Takada and Eganhouse, 1998 ). The presence of these compounds can provide information about the sources, transport, and fate of contaminants introduced by sewage (Eganhouse, 1997 ), and it is important to fundamentally maintain a water body for the management and quality of these sites. Sterols and hormones are EOCs found in low concentrations in the environment and are difficult to eliminate in water treatment processes (Jauković et al., 2017 ). However, their presence can be harmful to wildlife and consequently to humans (Jauković et al., 2017 ; Liu et al., 2009a ). Sterols are naturally produced by humans and animals and are used as fecal markers once their production occurs in the digestive system (Harwood, 2014 ). Coprostanol is the main constituent of human feces (approximately 60%) and is the main sterol used to detect human contamination in sewage (Mudge and Ball, 1964 ; Readman et al., 2005 ). It combines its concentration and the calculation of ratios of coprostanol to other sterols, such as cholesterol (Mudge and Duce, 2005 ), cholestanol (Grimalt et al., 1990 ), both combined (Chan et al., 1998 ), and its percentage (González-Oreja and Saiz-Salinas, 1998 ; Writer et al., 1995 ). Hormones are endocrine-disrupting compounds (EDCs) that can disrupt the endocrine system (Liu et al., 2009b ). According to Adeel et al. ( 2017 ), they can be synthetic, input in the environment by industrial activities, and irregular discards of human and veterinary medicines or natural by humans and animals (Lapworth et al., 2012 ). In wildlife, for example, EDCs can reduce growth, fertility, and reproduction, and may affect the sex of animals (Azizi-Lalabadi and Pirsaheb, 2021 ). In this sense, EDCs are part of the list of new candidates for contaminants of the American Environmental Protection Agency and the European Commission, and because of their greater use and characteristics, they appear to be notable anthropogenic markers, particularly for the indication of the origin of non-traditional anthropogenic activities (Lapworth et al., 2012 ; Adeel et al., 2017 ). Natural and synthetic hormones are categorized as EDCs, and the release of treated sewage is considered the main source of the environment; however, in recent years, numerous studies have shown that in addition to the contribution of urban activities, rural activities such as animal management have emerged as a considerable source (Adeel et al., 2017 ; Morais et al., 2019 ; Santos et al., 2019 , 2022 ). The presence of combined fecal sterols and EDCs in the environment can be a good indicator of spatial and temporal contamination by sewage, poor or lack of wastewater treatment, and other activities in the region (Froehner et al., 2012 ; Jauković et al., 2017 ). The environmental quality of most natural waters in Brazil is unknown because of the scarcity of studies on pollutants, specially in semiarid regions (Abessa et al., 2018 ). Estuaries are regions of important ecological and commercial value because they present high primary productivity and organic carbon, abundance of detritus (Froehner et al., 2012 ), and shelter many individuals for feeding, reproduction, resting, or living. Semiarid estuaries are unique, characterized by low discharge rates and disconnection between the upstream and downstream areas, including the main river courses, floodplains, and adjacent riverside areas, as a result of rainfall seasonality and recurrent droughts (Costa et al., 2013 ; Grill et al., 2019 ). In Northeast Brazil, the estuary Basins of Ceará State are impacted by dams located in high and medium river courses, constructed to provide water for human consumption. Consequently, the inflow of freshwater tributaries during the rainy season is reduced by up to 85%, thus affecting estuarine circulation and water renewal (Morais and Pinheiro, 2011 ). In this scenario, the transport of materials and chemical substances is affected and a longer residence time can increase the risk of adverse effects on estuarine biodiversity. The Acaraú River Basin is the second largest drainage basin in Ceará state and runs 28 cities. The draining area is affected by many kinds of activities (e.g., industry, farming and shrimp farming, fishing, and tourism) that can impact the river (Claudino-Sales et al., 2020 ). In addition, poor environmental sanitation services and sewage input directly into the river were observed in cities along the watercourse. Considering the Acaraú River Basin as a model for a semiarid estuary, the objective of this study was to assess the contributions of urban and rural activities to environmental quality status using anthropogenic molecular markers in superficial sediments. We hypothesized that land use and occupation along watersheds and drainage basins of semiarid regions would result in the input of chemicals that pose ecological risks according to a specific activity. Screening of contaminants of emerging concern supports the assessment of multiple human activities in understudied but ecologically relevant environments, providing a scientific baseline for management and conservation actions in these environments. Materials and methods Study area The Acaraú River Basin is located to the west of the capital of Ceará, running 28 cities, including the city of Sobral, with a total area of 14.500 km² (Araújo and Freire, 2013 ). A variety of anthropogenic activities occur along the basin, such as fishing, aquaculture, tourism, navigation, agro-extractivism, farming, industry, and dams (Claudino-Sales et al., 2020 ). All activities may affect the natural balance in the basin, and because most of them are located in the estuary, this environment is even more susceptible to degradation and pollution (Claudino-Sales et al., 2020 ; Nascimento et al., 2008 ). All sample sites were located near cities or communities, and to irregular sewage inputs. Some were located close to the aquaculture industry, farming, or animal husbandry. Sediment sampling Ten samples were collected on March along the Acaraú River between Varjota and Acaraú (Fig. 1 ). A superficial layer of 2 cm was collected using a stainless steel sampler. All samples were stored in aluminum packaging at a low temperature until arrival at the laboratory. Samples were dried in a stove at 65ºC for granulometry and calcium carbonate analysis. To extract organic contaminants and determine the total organic carbon content, the other samples were lyophilized. 1.1 Sedimentological and chemical analyses The wet sieving analysis method was used for the textural characterization of samples, and the black carbon (BC) content was determined by the thermal oxidation method, while the total organic carbon (TOC), humic acids (AH), and fulvic acids (AF) were determined according to the extraction, precipitation, and titration methodology (Suguio, 1973 ; Benites et al. 2003; Luz, 2007). Analysis of contaminants In this study, we determined the presence of four synthetic hormones (diethylstilbestrol -DES; 17αethinylestradiol − 17α-EE2; mestranol - MeEE2; and dienestrol – DIE), four natural hormones (17βestradiol – 17-βE2, 17αestradiol – 17-αE2, estriol - E3, and estrone - E1), and six sterols (cholesterol - COL, coprostanol - COP, cholestanol - CHOLN, stigmasterol - STG, β-sitosterol - β-SITO, and ergosterol - ERG). The previous stages for the analysis of compounds, extraction, clean-up, and derivatization are detailed in the publication of the developed method (Morais et al., 2019 ). Approximately 20 g of each dry sample was first spiked with a mixture of surrogates (5αandrostanol for sterols and estrone2,4d2 for hormones) and sonicated for extraction using different mixtures of solvents (dichloromethane/acetone/hexane ethyl acetate for 20 min, centrifuged, and concentrated to 1 ml. The extracts were purified using an adsorption open chromatographic column containing silica gel and alumina as the stationary phase and solvent mixtures (dichloromethane/hexane/methanol) as the mobile phase. The third fraction, which contained the analytes of interest, was separated, concentered to 1mL and derivatized to form trimethylsilyl ethers using 50µL of a mixture (99:1) of BSTFA (bis-trimethylsilyl-trifluoroacetamide and trimethylchlorosilane (TMCS) at 65°C for 90 min. The determination of sterols and EDCs was carried out using a GC–MS (Shimadzu model AP1010) with seven concentration levels (0.05 to 10.0 ng µL − 1 ), R 2 > 0.995, and the internal standard method was applied for the quantification of sterols and EDCs (Morais et al., 2019 ). All data were examined using a rigorous quality control procedure. All analytical systems used, including glassware, solvents, and materials, were free from contamination, as determined by reagent blank analysis. Recovery efficiency based on recovery levels of surrogate standards ranged from 55 to 95% for hormones (estrone-2,4-d2) and 63 to 105% for 5α-androstanol (sterols), and these values were similar to those found in further studies that used the same conditions (Pimentel et al., 2016 ; Lima et al., 2019 ; Santos et al., 2019 , 2022 ; Morais et al., 2020 ; Souza et al., 2022 ). Risk assessment Since natural and synthetic hormones are EDCs with bioactivity that lead to toxicity and adverse ecological effects, we performed an environmental risk assessment to estimate the potential of the concentrations found in sediments to induce sublethal effects on benthic organisms using the risk quotient (RQ) method (Staples et al., 2008 ; TGD, 2003 ). For each compound, the predicted no-effect concentration (PNEC) was obtained from species sensitivity distribution (SSD). First, data on aquatic toxicity were consulted in USEPA's ECOTOX Knowledgebase ( https://cfpub.epa.gov/ecotox/ ) using their respective CAS numbers as identifiers. The lowest observed effect concentrations (LOEC), which are the lowest concentrations statistically different from the control, were compiled along toxic endpoints estimated for 50% of the tested species (EC 50 and CL 50 ). An application factor of 0.01 was applied to convert acute toxicity into chronic toxicity equivalent (CCME, 1991) because chronic effects are often observed at environmental concentrations. SSD plots were constructed using the Shinyssdtools platform (Thorley and Schwarz, 2018 ; Dalgarno, 2018 ) by fitting the dataset into different distributions (gamma, log-Gumbel, log-logistic, log-normal, log-log-normal, and Weibull). Based on the goodness of fit, models with delta < 2 were selected for averaging multiple distributions (Burnham and Anderson 2002 ); thus, the hazard concentrations HC5, thresholds to protect 95% of the hypothetical community, were assigned as a PNEC. Due to the limited number of studies, 17α-estradiol included only freshwater data, while diethylstilbestrol, estrone, and estriol covered both saltwater and freshwater studies. 17α-ethinylestradiol and 17β-estradiol included only saltwater data. No data were found for dienestrol and mestranol and for these chemicals, the PNEC of the parental/similar compound, adjusted by the assessment factor of 1000, was used (EC, 2003 ). For 17α-estradiol, the geometric mean of the dataset (n = 2) also corrected by the assessment factor of 1000 was considered (EC, 2003 ). Since toxicity data were calculated using waterborne toxicity, hormone concentrations measured in sediments (C sed ) were corrected by the organic carbon partitioning coefficient (K oc ) of the comound and its respective fraction quantified in samples (f oc ) (MEC = C sed /(K oc x f oc ). The risk assessment consisted of ratios between the measured environmental concentrations of each hormone and their respective PNEC (RQ = MEC/PNEC). followed by the risk characterization as follows: low for RQ < 0.1), medium for RQ between 0.1 and 1(0.1 < RQ < 1), and high for RQ above 1 (RQ ≥ 1) (Blair et al., 2013 ). Results and discussion Sterols determination The individual and total concentrations (∑sterols) of the analyzed sterols are presented in Table 1 and in Supplementary Information (Table 4). All sterols were detected at all stations, and the ∑sterols ranged from 271.5 to 2525 ng g -1 . The sterol with the highest concentration was β-SITO, especially in the estuarine region (ACR8 to ACR10), which is plausible because mangrove plants are the main source of this natural sterol (Santos et al., 2019 ; Volkman et al., 2007 ). The levels of ERG, followed by COL, were also high, which must be attributed to most of the points being in rural areas. As reported by Weete ( 1973 ), the presence of ERG levels is due to fungal decomposition, while the COL comes from various sources, such as some types of marine algae organisms and animal lipids. However, it may be related to human and animal feces and the dealkylation of C28 and C29 phytosterols promoted by zooplankton species in areas far from the coast (Volkman, 1986; Prost et al., 2017). Table 1 Individual and total sterol concentrations and diagnostic ratios in surface sediments of the Acaraú River basin (ng g − 1 ). Compounds ACR01 ACR02 ACR03 ACR04 ACR05 ACR06 ACR07 ACR08 ACR09 ACR10 Sterols COP 83.18 32.85 6.0 22.4 14.98 13.49 40.28 50.18 79.24 124.1 COL 754.6 191.6 120.5 213.3 224.8 241.4 218.1 288.8 215.9 438.2 CHOLN 120.2 66.96 9.38 15.68 14.67 63.9 143.0 32.05 13.4 188.3 ERG 442.1 431.0 43.0 89.81 105.2 172.9 567.7 68.24 204.7 885.7 STIG 218.9 130.6 22.18 39.64 38.03 32.87 213.7 207.9 306.8 209.3 β-SITO 386.2 492.0 70.47 178.8 178.9 145.3 379.8 714.1 766.7 679.4 ΣSterols 2005.0 1345.0 271.5 559.7 576.6 669.8 1563.0 1361.0 1587.0 2525.0 Ratios Cop/Col 0.11 0.17 0.05 0.11 0.07 0.06 0.18 0.17 0.37 0.28 Cop/(Cop + Choln) 0.41 0.33 0.39 0.59 0.51 0.17 0.22 0.61 0.86 0.4 Cop/Choln 0.69 0.49 0.64 1.43 1.02 0.21 0.28 1.57 5.91 0.66 Cop/(Choln + Col) 0.1 0.13 0.05 0.1 0.06 0.04 0.11 0.16 0.35 0.2 Choln/Col 0.16 0.35 0.08 0.07 0.07 0.26 0.66 0.11 0.06 0.43 COP is the most abundant sterol in human feces and is due to the fact that they are biosynthesized from the conversion of CHOL by bacterial reduction in the intestine of higher animals, and therefore are considered the main indicator of sewage input into the environment (Leeming and Nichols, 1996). The concentration of COP ranging from 6 to 124.1 ng g − 1 in Acaraú River Basin showed concentrations closest to less inhabited areas in Brazil as Bitupitá, São Caetano de Odivelas, Barra de São Miguel and lower than values found in more developed cities or estuaries with any industrial activity as Rio de Janeiro, Cubatão, Maceió, Natal and Florianópolis and other (Cordeiro et al., 2008 ; Campos et al., 2012 ; Abreu-Mota et al., 2014 ; Martins et al., 2014 ; Araújo et al., 2021 ; Santos et al., 2008 , 2019 ). The concentration of COP represented 3.1% of the Σsterols, showing low contamination by sewage and possibly associated with the presence of input from rural activities (Santos et al., 2019 ). Several studies use diagnostic ratios to identify and distinguish inputs from multiple sources as well as assessment the influence of anthropogenic activities on the development of the region (Table 2 ). Table 2 Diagnostic ratios used for samples from the Acaraú River Basin. Diagnostic ratios Threshold levels Environment status Ratio 1 a 0.2–1 > 1 Sewage-contaminated Highly contaminated Ratio 2 b 0.4 Uncontaminated Fecal contamination Ratio 3 c 0.7 Uncontaminated Sewage-contaminated Ratio 4 d 0.20 Uncontaminated Fecal contamination Ratio 5 e < 0.5 Fresh organic matter input COP. f 500 ng.g -1 Uncontaminated Sewage-contaminated a Coprostanol/cholesterol ratio (Takada et al., 1994) b Coprostanol/cholestanol ratio (Shah et al., 2007 ) c Coprostanol/(coprostanol + cholestanol) ratio (Grimalt et al., 1990 ) d Coprostanol/(cholestanol + cholesterol) ratio (Chan et al., 1998 ) e Colestanol/cholesterol ratio (Canuel and Martens, 1993 ) f Coprostanol levels (González-Oreja and Saiz-Salinas, 1998 ) According to Takada et al. (1994), the Ratio 1 values greater than 1 indicate highly contaminated sites, while values ranging from 0.2 to 1 indicate sites contaminated with sewage. As can be seen in Table 1 , the Ratio 1 ranged from 0.05 to 0.30, characterizing the areas studied as being sewage-contaminated. The Ratio 2 values > 0.4 indicate human fecal contamination (Shah et al., 2007 ), and as can be seen in Table 2 , it ranged from 0.17 to 0.86, a characteristic of fecal contamination in most of the environments studied (ACR01, ACR04, ACR05, ACR08, ACR09 and ACR010). In the Ratio 3 values 0.7 characterize sewage-contaminated (Grimalt et al., 1990 ). In Acaraú river the characterization of the contamination was not homogeneous, the places characterized as contaminated were ACR04, ACR05, ACR08 and ACR09, and others not contaminated only sites 6 and 7, while four places cannot be characterized. According to Abreu-Mota et al. ( 2014 ), the reduced form of COL in the environment is CHOLN indicating inconclusive input. In the Ratio 4 0.20 indicates contamination by fecal matter (Chan et al., 1998 ). The Ratio 4 indicated a site uncontaminated by sewage in most of the sites studied (ACR01 toACR07), while in two locations it shows highly contaminated (ACR09 andACR10). According to Canuel and Martens ( 1993 ) the Ratio 5 values < 0.5 indicate fresh organic matter input, while ranged from 0.06 to 0.43 is indicative from unsaturated form with respect to its saturated homologue. COP levels higher than 500 ng g-1 is considered as an indicator of sewage contamination, while levels less than 100 ng g-1 are considered indicative of a natural site (Writer et al., 1995 ; González-Oreja and Saiz-Salinas, 1998 ). None of the stations showed concentrations larger than this value, except station ACR10 which certainly receives influence from sewage. EDCs determination Natural and synthetic EDCs were detected at all stations (Fig. 2 and Supplementary Information, Table 5 ). The average total concentrations (∑nat. hormones) was 21.78 ng g -1 , while ∑synt. hormones was 117.45 ng g -1 and were quantified in higher concentrations than ∑nat. hormones at all sample sites. The higher values detected for synthetic hormones may be related to their lower water solubility and higher log Kow, preferentially partitioning to sediment than to water (Caldwell et al., 2012 ; Matić et al., 2014 ).In general, levels of hormones are usually larger in estuaries from Brazil than in other countries which can be explained due to the lack of waste treatment (Arditsoglou and Voutsa, 2012 ; Froehner et al., 2011 ; Gorga et al., 2015 ; Isobe et al., 2006 ; Morais et al., 2019 ; Pimentel et al., 2016 ; Santos et al., 2019 , 2022 ; Zhang et al., 2009 ). According to Andaluri et al. ( 2012 ) estimative indicate that the global human population contributes approximately 31,000 kg year -1 of natural steroid estrogens, while only the European Union and the USA are responsible for an annual discharge of estrogen by the livestock sector of approximately 84,000 kg year -1 , more than two times higher than the human discharge rate (Adeel et al., 2017 ). Another cause can be the number of compounds examined in each study, provoking the higher presence of natural hormones in comparison with synthetic hormones, usually represented by one or two compounds (Lei et al., 2009 ; Liu et al., 2017 ; Wang et al., 2015 ). Another important point is the fact that the synthetic EDCs studied have high log Kow ranging from 4.1 to 5.6 compared to natural EDCs (log Kow 2.8 to 3.9), and therefore the environmental partition is expected to be preferentially for the sediment compartment, especially those sediments rich in organic matter (Lei et al., 2009 ; Caldwell et al., 2012 ). 17α-E2 (nd − 208.4 ng g -1 ), 17β-E2 (nd − 29.5 ng g -1 ), E1 (nd − 73.83 ng g -1 ) and E3 (0.07–25.41 ng g -1 ) concentrations were 5.4%, 1.2%, 5.4% and 2% of ∑hormones, respectively, and ∑nat. hormones were responsible for 14% of patients. These hormones are produced and excreted by humans and animals (Ternes et al., 1999 ), and are easily degraded by bacteria at sites with abundant nutrients (Lintelmann et al., 2003 ). Estriol was the only natural hormone present in all stations. This chemical is also used in clinical treatments (Ali et al., 2017 ) and swine farms (Adeel et al., 2017 ), and is classified as harmful to the environment (Carlsson et al., 2006 ). The values found in the Acaraú River Basin are much lower than those in estuaries with high industrial activity and larger populations (Pusceddu et al., 2019 ), but still higher than those in other countries (Isobe et al., 2006 ; Arditsoglou and Voutsa, 2012 ). Mestranol and 17αethinylestradiol ranged from 2.77 to 251.7 ng g -1 and from 51.2 to 153.8 ng g -1 , respectively. Dienestrol ranged from nd to 50.5 ng g -1 and was the least abundant synthetic hormone detected. Diethylstilbestrol (DES) was the compound with the highest representativeness among all the hormones (46% of ∑hormones), ranging from 3.29 to 1474 ng g -1 . DES was first prescribed to pregnant women in the 1940s to prevent complications in pregnancy and later to prevent menopausal symptoms (Jama, 1976 ). in the 70s was found to cause adverse effects such as cancer (Herbst et al., 1971 ). DES is still used in livestock and aquaculture, which is a means of contaminating natural environments and food (Adedeji et al., 2012 ; Yang et al., 2012 ; Wei et al., 2018 ). Environmental distribution and sources of sterols and EDCs According to Oliveira et al. ( 2016 ), few studies have investigated the effects of sediment components on the destination and distribution of organic contaminants, which should contribute significantly to explaining the patterns of environmental and geographic distribution. According to Luthy et al. ( 1997 ), there is consensus that the accumulation of organic contaminants in sediments is governed by sorption on amorphous organic matter (e.g., fulvic and humic acids, lignin, proteins, and others) and carbonaceous materials (e.g., coal, black coal, and kerogen) (Cornelissen et al., 2005 ). Table 3 presents the correlations among sterols, EDCs, and inorganic and organic sediment composition parameters. The ∑sterols and the majority of the individual sterols were moderate (0.4 to 0.59) to strong (0.60 to 0.79) (Evans, 1996 ), and were significantly correlated with the fine-grained sediments (silt or clay) and the organic parameters (TOC, HA/TOC, and FA/TOC). Table 3 Spearman's rank correlation coefficient (R2, p < 0.01) for the dependence of compounds and sediment components. Compound Grain size fractions Organic parameters Sand (%) Silt (%) Clay (%) TOC (%) HA (%) FA (%) BC (%) DES 0.56 DIE MeEE2 17-αEE2 17α-E2 17β-E2 E1 -0.51 0.61 0.55 0.63 0.58 0.58 E3 ∑ Sint. Hormones ∑ Nat. Hormones ∑ Hormones COP -0.70 0.82 0.83 0.75 0.52 0.79 CHOL CHOLESTAN -0.43 0.46 0.53 0.56 ERG -0.52 0.50 0.59 0.66 0.61 ESTIG -0.48 0.62 0.52 0.40 0.64 0.45 B-SITO -0.67 0.82 0.70 0.64 0.88 0.60 ∑sterols -0.60 0.69 0.68 0.66 0.53 0.66 The COP presented the highest positive correlation with fine-grained sediments and associated organic parameters. Among the sterols, only COL showed no significant correlation with the inorganic or organic sediment parameters. Several authors have indicated that fecal and phytosterols are highly hydrophobic, are mainly adsorbed and transported by fine suspended particles, and accumulate in sediments with high levels of TOC (Tolosa et al., 2014 ; Speranza et al., 2018 ; Souza et al., 2020 ; Santos et al., 2022 ; Gomes et al., 2023 ). Therefore, considering that the analyzed sterols presented specific sources of organic matter (aquatic, terrestrial, and anthropogenic), the positive and significant correlations suggest that the distribution of fine-grained sediments and their organic parameters are decisive factors driving the accumulation of sterols, especially the COP, along the Acaraú River. Of all the analyzed EDCs, only E1 presented a significant correlation with the inorganic and organic parameters of the sediment. The absence of a correlation between the majority of the estrogenic hormones and the sediment composition parameters is because these compound distributions are more related to the anthropogenic input from point sources of pollution than the sediment characteristics. Black carbon (BC) is a known natural adsorbent in aquatic environments; however, only DES showed a significant correlation. BC is likely to originate from urban activities, such as fossil fuel use and biomass as energy sources in productive activities (e.g., bakeries, steakhouses, and brickworks), whereas in rural regions, the contribution comes from fires such as land management, as well as burning wood and coal as a source of energy in production processes (e.g., manufacture of red ceramics) and residential areas (Cavalcante et al., 2011 ; Santos et al., 2019 ). Several studies show low or no correlation of organic contaminants with BC in terms of environmental distribution and this is due to BC being linked to urban and rural sources throughout the geographic range (Oliveira et al., 2016 ; Gama et al., 2017 ; Andrade et al., 2019 ). In contrast, the studied contaminants have their inputs at specific points linked to urban or rural sources separately. To understand the influence of the various anthropogenic activities in the region, we applied an integrated environmental assessment using sterol and EDCs levels to provide a more faithful idea of the situation of the environment. Therefore, the relative contribution of the main activities was quantified by source apportionment using PCA with land-use classification (Fig. 3 ). The first component (PC1) accounted for 35.5% and the second component (PC2) accounted for 30.7% of the total variance (Fig. 3 a). PC1 separated stations ACR01, ACR07, ACR08, ACR09, and ACR10 from stations ACR02, ACR03, ACR04, ACR05, and ACR06, whereas PC2 separated stations ACR01, ACR02, ACR04, ACR05, ACR08, and ACR09 from stations ACR03, ACR06, ACR07, and ACR10 (Supplementary Information Fig. 5). PC1 performed with diagnostic ratios of steroids and concentrations of natural and synthetic EDCs grouped stations of a greater fluvial nature than those considered coastal (except for ACR01). The PC1was positively loaded with all chemical substances used as markers. Therefore, PC1 positive values can be associated with a mixture of natural environment and pollution by input of waste from rural and urban activities at stations ACR01, ACR07, and ACR10. In relation to PC2 it was negatively loaded by Ratio 1 to Ratio 4, as well by DES, DIE, E1 and COP, and thus, negative values for PC2 also indicate mixture of pollution at stations ACR01, ACR02, ACR04, ACR05, ACR08 and ACR09. On the other hand, was positively loaded by MeEE2, 17-αEE2, 17α-E2, 17β-E2, and E3 on ACR03, ACR06, ACR07, and ACR10 also showed a mixture of sources. PC1 explained the environmental condition in relation to the input related to rural activities, especially those using drugs in animal management, because it was responsible for the largest total variances, and also because it grouped stations ACR01, ACR07 to ACR10. The ACR01 station is right at the exit of Açude Varjota (water reservoir), and since the 1990s projects for the use of fish farming have been encouraged in these water bodies (Sousa at al., 2016), while regarding points ACR07 to ACR10, shrimp farms are the main economic activities in this region. The impacts of rural activities, which have been less studied as sources of pollution, are increasingly emerging. DES detected in sewage in China has been attributed to the overuse of hormones and/or their illegal use in animal feed (Jin et al. 2008). The highest levels of natural hormones were found in areas influenced by cities, whereas synthetic hormones were found only in areas influenced by rural-urban inputs (Liu et al., 2017 ). Rural activities, especially fish and shrimp farming and livestock, are responsible for the largest contribution of synthetic EDCs in the environment (e.g., DIE, DES, αEE2, and MeEE2) and natural hormones (e.g., αE2, E1, and βE2) in tropical mangroves with a restricted predominance of human sewage (Santos et al., 2019 ). The levels of synthetic EDCs used as contraceptives began to be recorded in a sediment core in one of the largest rivers in the Brazilian semi-arid region (Rio Jaguaribe) since the 60s, while the hormones used in animal management after the 80s, which was attributed to the development of rural activities such as livestock, shrimp, and fish farming (Santos et al., 2022 ). Nevertheless, the Acaraú region does not have an economic culture based on livestock; therefore, the data indicate that the main economic activities, such as fish and shrimp farming that use drugs in animal husbandry, are certainly the predominant anthropic sources of EDCs in the region, even surpassing the input from classic urban activities, such as chronic release of treated or untreated sewage. The data also show areas of the river that are only impacted by urban activity, such as the release of untreated sewage or inefficient treatment systems, and areas that are impacted by both urban and rural activities, such as fish and shrimp farming, which are predominant in the region. On the one hand, the stations located in ACR04 and ACR08 to ACR10 are the most populous cities; therefore, the levels of markers linked to sewage are justified due to the insufficient structure of the basic sanitation systems of these cities (< 50%, IBGE). The rest of the stations ACR02, ACR03, ACR04, and ACR06 are sparsely populated cities, and do not have developed agricultural activities, and thus contribute to the environment only with markers linked to urban activity, especially because of the fragile structure of basic sanitation systems (5 to 20%, IBGE 2023). According to Morais et al. (2018), the evidence that sterols, especially those linked to fecal input, not only come from cities with insufficient or no sewage treatment, but COP levels in the final effluent, depending on the treatment time in the sewage treatment plants, can be in the magnitudes of the first to the eighth most abundant sterol (Neur et al., 1994; Reichwaldt et al., 2016) (Fig. 3 ). Risk assessment In this study, we performed a risk assessment based on RQ calculations for the contamination levels of hormones quantified in sediment samples from the Acaraú River, since this group of substances is classified as EDC. For each compound, the compilation of waterborne toxicity data is detailed in the appendix along with the parameters for the RQ calculations (Supplementary Information Tables 6 to 11 and Fig. 6). The RQ results showed that all samples produced a low risk of adverse effects for diethylstilbestrol, estriol, and 17α-estradiol. The concentrations of dienestrol (ACR-03, ACR-07, and ACR-08), 17β-estradiol (ACR-07), and estrone (ACR-03 and ACR-09) showed medium risk. A high risk of adverse effects (RQ ≥ 1) was observed in all samples for 17α-ethinylestradiol, mestranol and estrone (except ACR-03 and ACR-09), in addition to dienestrol (ACR-01 and ACR-09) and 17α-Estradiol (ACR-01 and ACR-07) (Fig. 4 and Supplementary Information Tables 12 and 13). It is important to highlight that the ERA was based on liquid-phase toxicity due to the limited number of studies targeting the sediment toxicity of all hormones. However, the results indicate that 17α-ethinylestradiol, mestranol and estrone are compounds of environmental concern in the Acarú River, which is consistent with the literature regarding endocrine-disrupting substances in the environment. For instance, the issue of feminized fish has been related to the presence of estrogens in the aquatic ecosystem, particularly the steroids 17β-estradiol and 17α-ethinylestradiol as the main compounds, along with the natural hormone estrone, which potentially contributes to estrogenicity (Ankley et al. 2017 ). According to Pimentel et al. ( 2016 ), the studied region already has examples of evidence of fish feminization attributed to high levels of EDCs. Estrogenic compounds can bind to estrogen receptors (ER) at the molecular level, triggering an adverse outcome pathway that can alter reproduction at the whole-organism level, with consequent effects on populations. In such a process, other biological responses may occur following ER activation, including gene expression alterations, variations in sex steroid and vitellogenin levels (e.g., egg yolk protein), gonad deformities, alterations in secondary sex characteristics, and reproductive behavior (Ankley et al. 2010 ). In addition to the effects of the estrogen signaling pathway, non-specific responses have been reported for 17α-ethinylestradiol and estrone in the sediments. Bioaccumulation of the 17a-ethinylestradiol in Chironomus dilutus and Hyalella azteca was reported in exposures with freshwater spiked samples (Dussault et al. 2009 ), indicating a potential uptake of the compound by benthic organisms, but no toxicity on Survival and growth was induced in both species at 1.2 to 7.6 mg Kg -1 (Gilroy et al. 2012 ). In marine sediments spiked with 0.01 to 100 ng g -1 of 17a-ethinylestradiol, no toxicity was reported for the amphipod Ampelisca brevicornis , but significant responses were observed on the bioluminescence inhibition of the bacteria Vibrio fischeri (IC50 of 39.4 ng g -1 ) in solid phase experiments (Maranho et al. 2015a ). In the same study, elutriates produced spermiotoxicity and embryotoxicity to the sea urchin Paracentrotus lividus (LOEC of 0.01 ng g -1 ), and impaired growth rate of the microalgae Isochrysis galbana and Tetraselmis chuii at 1 ng g -1 . At the sublethal level, the energy status of the polychaete Hediste diversicolor was not affected by 17α-ethinylestradiol, but the activities of monoamine oxidase (neuroendocrine response) and cyclooxygenase (inflammatory process mediator) were altered following the 14-day exposure of sediment spiked with concentrations from 0.01 to 100 ng g -1 (Maranho et al. 2015b ). Thus, we consider that the inefficient sewage treatment system combined with rural activities releases hormones to sediments, impairing their quality and posing risks to benthic organisms. Conclusion By using an anthropogenic molecular marker approach, we demonstrated that land use conditions in a semi-arid estuary resulted in the input of chemicals into the system related to urban and rural activities. In some areas, the absence of an efficient sewage treatment system has contributed to the impact along the Acarú River. On the other hand, the study showed that in regions of the lower estuary that have intense rural activity, the main sources of synthetic EDC are linked to fish and shrimp farming, surpassing even the contribution from sewage effluents. The data showed that 17α-ethinylestradiol, mestranol, and estrone are compounds of environmental concern in the Acarú River, and this is an important baseline for further assessment, pollution control policies, and conservation decision-making. Declarations Author Contribution All authors were involved in the conception and design of the study and reviewed the final manuscript version. R.M.C. is the Principal Investigator responsible for the Conceptualization, Investigation, Project administration, visualization, and writing of the original draft. A.D.S.P., M.F.B.L., E.V.M., P.M., G.M.F., R.P.S., R.F.N., and F.R.S. participated in the conceptualization, investigation, and formal analysis, and wrote the original draft. L.B.M. participated in the investigation, visualization, and formal analysis, wrote the manuscript, and edited the manuscript. Acknowledgement This study was funded by the Brazilian National Council for Scientific and Technological Development (CNPq), the Financier of Studies and Projects (FINEP), the Ceará Foundation for Support to Scientific and Technological Development (FUNCAP), and the São Paulo Research Foundation (FAPESP). R.M. Cavalcante is grateful for the PQ-2 (Grant number 315281/2020-0-CNPq), as well as Lemae/Finep/CNPq (Grant number 380975/2022–0), Pronen/FUNCAP/CNPq (Grant number Pne-0112-00007.01.00/16) and Pronex (Grant number PR2 0101 00008 01 00/15). The authors thank the members of LACOr for their enthusiasm and energy related to scientific work. “essas pessoas são as melhores”. L.B. Moreira was funded by FAPESP (Grant numbers 2020/00068-8 and 2021/08471-9). References Abessa, D.M.S., Albuquerque, H.C., Morais, L.G., Araújo, G.S., Fonseca, T.G., Cruz, A.C.F., Campos, B.G., Camargo, J.B.D.A., Gusso-Choueri, P.K., Perina, F.C., Choueri, R.B., & Buruaem, L.M. (2018). 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(2007). Sources of organic matter in sediments from the Ord River in tropical northern Australia. Organic Geochemistry 38, 1039–1060. https://doi.org/10.1016/j.orggeochem.2007.02.017 Wakeham, S.G., & Canuel, E.A. (1990). Fatty acids and sterols of particulate matter in a brackish and seasonally anoxic coastal salt pond. Organic Geochemistry 16, 703–713. https://doi.org/10.1016/0146-6380(90)90111-C Wang, Y., Wang, Q., Hu, L., Lu, G., & Li, Y. (2015). Occurrence of estrogens in water, sediment and biota and their ecological risk in Northern Taihu Lake in China. Environmental Geochemistry and Health 37, 147–156. https://doi.org/10.1007/S10653-014-9637-0/TABLES/4 Weete, J.D. (1973). Sterols of the fungi: distribution and biosynthesis. Phytochemistry 12,1843–1864. https://doi.org/10.1016/S0031-9422(00)91502-4 . Wei, L., Yan, Y., Deng, J., Ma, Y., Wang, Y., Wu, X., & Kang, X. (2018). Determination of Estrogens in Milk Using Polypyrrole Fiber-Mediated Solid-Phase Extraction Followed by High Performance Liquid Chromatography. Journal of the Brazilian Chemical Society 29(10), 2137–2143. https://doi.org/10.21577/0103-5053.20180088 Writer, J.H., Leenheer, J.A., Barber, L.B., Amy, G.L., & Chapra, S.C. (1995). Sewage contamination in the upper Mississippi River as measured by the fecal sterol, coprostanol. Water Research 29, 1427–1436. https://doi.org/10.1016/0043-1354(94)00304-P Yang, Y., Chen, J., & Shi, Y.-P. (2012). Determination of diethylstilbestrol in milk using carbon nanotube-reinforced hollow fiber solid-phase microextraction combined with high-performance liquid chromatography. Talanta 97, 222–228. https://doi.org/10.1016/j.talanta.2012.04.021 Zhang, X., Li, Q., Li, G., Wang, Z., & Yan, C. (2009). Levels of estrogenic compounds in Xiamen Bay sediment, China. Marine Pollution Bulletin 58, 1210–1216. https://doi.org/10.1016/j.marpolbul.2009.03.011 Additional Declarations No competing interests reported. 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At least 25% of the Brazilian population lives in coastal cities (Szlafsztein and Sterr, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which is a consequence of tourism and the economic potential associated with natural coastal areas, making them vulnerable, including estuaries. Anthropic and industrial activities are the main sources of pollutants introduced to the environment by sewage. This has added to the lack of effective wastewater (Sato et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and the input of sewage is a major concern in coastal management.\u003c/p\u003e\u003cp\u003eThe presence of sewage in natural waters frequently causes eutrophication, oxygen depletion (Fisher et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), diminished balnealbility, and other problems. Furthermore, some substances present in sewage are difficult to treat, and some are not legally regulated by countries. Persistent Organic Pollutants (POPs), Emergent Organic Contaminants (EOCs), and natural and synthetic organic compounds that are present in rivers or seas may compromise the quality of water and sediments and cause adverse effects on fauna, flora, and human health (Rosenfeld and Feng, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These substances can be found as components or active ingredients in many products, such as human and veterinary medicines, industry, personal care products, hormones and steroids, and food additives (Richardson and Ternes, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lapworth et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnthropogenic Molecular Markers are natural and anthropogenic compounds whose origin can provide an environmental diagnostic by being persistent and source-specific (Takada and Eganhouse, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The presence of these compounds can provide information about the sources, transport, and fate of contaminants introduced by sewage (Eganhouse, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and it is important to fundamentally maintain a water body for the management and quality of these sites.\u003c/p\u003e\u003cp\u003eSterols and hormones are EOCs found in low concentrations in the environment and are difficult to eliminate in water treatment processes (Jauković et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, their presence can be harmful to wildlife and consequently to humans (Jauković et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e). Sterols are naturally produced by humans and animals and are used as fecal markers once their production occurs in the digestive system (Harwood, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Coprostanol is the main constituent of human feces (approximately 60%) and is the main sterol used to detect human contamination in sewage (Mudge and Ball, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1964\u003c/span\u003e; Readman et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It combines its concentration and the calculation of ratios of coprostanol to other sterols, such as cholesterol (Mudge and Duce, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), cholestanol (Grimalt et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), both combined (Chan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and its percentage (Gonz\u0026aacute;lez-Oreja and Saiz-Salinas, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Writer et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHormones are endocrine-disrupting compounds (EDCs) that can disrupt the endocrine system (Liu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e). According to Adeel et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), they can be synthetic, input in the environment by industrial activities, and irregular discards of human and veterinary medicines or natural by humans and animals (Lapworth et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In wildlife, for example, EDCs can reduce growth, fertility, and reproduction, and may affect the sex of animals (Azizi-Lalabadi and Pirsaheb, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this sense, EDCs are part of the list of new candidates for contaminants of the American Environmental Protection Agency and the European Commission, and because of their greater use and characteristics, they appear to be notable anthropogenic markers, particularly for the indication of the origin of non-traditional anthropogenic activities (Lapworth et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Adeel et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Natural and synthetic hormones are categorized as EDCs, and the release of treated sewage is considered the main source of the environment; however, in recent years, numerous studies have shown that in addition to the contribution of urban activities, rural activities such as animal management have emerged as a considerable source (Adeel et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morais et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe presence of combined fecal sterols and EDCs in the environment can be a good indicator of spatial and temporal contamination by sewage, poor or lack of wastewater treatment, and other activities in the region (Froehner et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jauković et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The environmental quality of most natural waters in Brazil is unknown because of the scarcity of studies on pollutants, specially in semiarid regions (Abessa et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Estuaries are regions of important ecological and commercial value because they present high primary productivity and organic carbon, abundance of detritus (Froehner et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and shelter many individuals for feeding, reproduction, resting, or living. Semiarid estuaries are unique, characterized by low discharge rates and disconnection between the upstream and downstream areas, including the main river courses, floodplains, and adjacent riverside areas, as a result of rainfall seasonality and recurrent droughts (Costa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Grill et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Northeast Brazil, the estuary Basins of Cear\u0026aacute; State are impacted by dams located in high and medium river courses, constructed to provide water for human consumption. Consequently, the inflow of freshwater tributaries during the rainy season is reduced by up to 85%, thus affecting estuarine circulation and water renewal (Morais and Pinheiro, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this scenario, the transport of materials and chemical substances is affected and a longer residence time can increase the risk of adverse effects on estuarine biodiversity.\u003c/p\u003e\u003cp\u003eThe Acara\u0026uacute; River Basin is the second largest drainage basin in Cear\u0026aacute; state and runs 28 cities. The draining area is affected by many kinds of activities (e.g., industry, farming and shrimp farming, fishing, and tourism) that can impact the river (Claudino-Sales et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, poor environmental sanitation services and sewage input directly into the river were observed in cities along the watercourse. Considering the Acara\u0026uacute; River Basin as a model for a semiarid estuary, the objective of this study was to assess the contributions of urban and rural activities to environmental quality status using anthropogenic molecular markers in superficial sediments. We hypothesized that land use and occupation along watersheds and drainage basins of semiarid regions would result in the input of chemicals that pose ecological risks according to a specific activity. Screening of contaminants of emerging concern supports the assessment of multiple human activities in understudied but ecologically relevant environments, providing a scientific baseline for management and conservation actions in these environments.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Acara\u0026uacute; River Basin is located to the west of the capital of Cear\u0026aacute;, running 28 cities, including the city of Sobral, with a total area of 14.500 km\u0026sup2; (Ara\u0026uacute;jo and Freire, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). A variety of anthropogenic activities occur along the basin, such as fishing, aquaculture, tourism, navigation, agro-extractivism, farming, industry, and dams (Claudino-Sales et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). All activities may affect the natural balance in the basin, and because most of them are located in the estuary, this environment is even more susceptible to degradation and pollution (Claudino-Sales et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nascimento et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). All sample sites were located near cities or communities, and to irregular sewage inputs. Some were located close to the aquaculture industry, farming, or animal husbandry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSediment sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTen samples were collected on March along the Acara\u0026uacute; River between Varjota and Acara\u0026uacute; (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A superficial layer of 2 cm was collected using a stainless steel sampler. All samples were stored in aluminum packaging at a low temperature until arrival at the laboratory. Samples were dried in a stove at 65\u0026ordm;C for granulometry and calcium carbonate analysis. To extract organic contaminants and determine the total organic carbon content, the other samples were lyophilized.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1.1 Sedimentological and chemical analyses\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe wet sieving analysis method was used for the textural characterization of samples, and the black carbon (BC) content was determined by the thermal oxidation method, while the total organic carbon (TOC), humic acids (AH), and fulvic acids (AF) were determined according to the extraction, precipitation, and titration methodology (Suguio, \u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e; Benites et al. 2003; Luz, 2007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of contaminants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we determined the presence of four synthetic hormones (diethylstilbestrol -DES; 17\u0026alpha;ethinylestradiol \u0026minus;\u0026thinsp;17\u0026alpha;-EE2; mestranol - MeEE2; and dienestrol \u0026ndash; DIE), four natural hormones (17\u0026beta;estradiol \u0026ndash; 17-\u0026beta;E2, 17\u0026alpha;estradiol \u0026ndash; 17-\u0026alpha;E2, estriol - E3, and estrone - E1), and six sterols (cholesterol - COL, coprostanol - COP, cholestanol - CHOLN, stigmasterol - STG, \u0026beta;-sitosterol - \u0026beta;-SITO, and ergosterol - ERG).\u003c/p\u003e\n\u003cp\u003eThe previous stages for the analysis of compounds, extraction, clean-up, and derivatization are detailed in the publication of the developed method (Morais et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Approximately 20 g of each dry sample was first spiked with a mixture of surrogates (5\u0026alpha;androstanol for sterols and estrone2,4d2 for hormones) and sonicated for extraction using different mixtures of solvents (dichloromethane/acetone/hexane ethyl acetate for 20 min, centrifuged, and concentrated to 1 ml.\u003c/p\u003e\n\u003cp\u003eThe extracts were purified using an adsorption open chromatographic column containing silica gel and alumina as the stationary phase and solvent mixtures (dichloromethane/hexane/methanol) as the mobile phase. The third fraction, which contained the analytes of interest, was separated, concentered to 1mL and derivatized to form trimethylsilyl ethers using 50\u0026micro;L of a mixture (99:1) of BSTFA (bis-trimethylsilyl-trifluoroacetamide and trimethylchlorosilane (TMCS) at 65\u0026deg;C for 90 min.\u003c/p\u003e\n\u003cp\u003eThe determination of sterols and EDCs was carried out using a GC\u0026ndash;MS (Shimadzu model AP1010) with seven concentration levels (0.05 to 10.0 ng \u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.995, and the internal standard method was applied for the quantification of sterols and EDCs (Morais et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). All data were examined using a rigorous quality control procedure. All analytical systems used, including glassware, solvents, and materials, were free from contamination, as determined by reagent blank analysis. Recovery efficiency based on recovery levels of surrogate standards ranged from 55 to 95% for hormones (estrone-2,4-d2) and 63 to 105% for 5\u0026alpha;-androstanol (sterols), and these values were similar to those found in further studies that used the same conditions (Pimentel et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lima et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Santos et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Morais et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Souza et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince natural and synthetic hormones are EDCs with bioactivity that lead to toxicity and adverse ecological effects, we performed an environmental risk assessment to estimate the potential of the concentrations found in sediments to induce sublethal effects on benthic organisms using the risk quotient (RQ) method (Staples et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; TGD, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). For each compound, the predicted no-effect concentration (PNEC) was obtained from species sensitivity distribution (SSD). First, data on aquatic toxicity were consulted in USEPA\u0026apos;s ECOTOX Knowledgebase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cfpub.epa.gov/ecotox/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e using their respective CAS numbers as identifiers. The lowest observed effect concentrations (LOEC), which are the lowest concentrations statistically different from the control, were compiled along toxic endpoints estimated for 50% of the tested species (EC\u003csub\u003e50\u003c/sub\u003e and CL\u003csub\u003e50\u003c/sub\u003e). An application factor of 0.01 was applied to convert acute toxicity into chronic toxicity equivalent (CCME, 1991) because chronic effects are often observed at environmental concentrations.\u003c/p\u003e\n\u003cp\u003eSSD plots were constructed using the Shinyssdtools platform (Thorley and Schwarz, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dalgarno, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) by fitting the dataset into different distributions (gamma, log-Gumbel, log-logistic, log-normal, log-log-normal, and Weibull). Based on the goodness of fit, models with delta\u0026thinsp;\u0026lt;\u0026thinsp;2 were selected for averaging multiple distributions (Burnham and Anderson \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e); thus, the hazard concentrations HC5, thresholds to protect 95% of the hypothetical community, were assigned as a PNEC. Due to the limited number of studies, 17\u0026alpha;-estradiol included only freshwater data, while diethylstilbestrol, estrone, and estriol covered both saltwater and freshwater studies. 17\u0026alpha;-ethinylestradiol and 17\u0026beta;-estradiol included only saltwater data. No data were found for dienestrol and mestranol and for these chemicals, the PNEC of the parental/similar compound, adjusted by the assessment factor of 1000, was used (EC, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). For 17\u0026alpha;-estradiol, the geometric mean of the dataset (n\u0026thinsp;=\u0026thinsp;2) also corrected by the assessment factor of 1000 was considered (EC, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Since toxicity data were calculated using waterborne toxicity, hormone concentrations measured in sediments (C\u003csub\u003esed\u003c/sub\u003e) were corrected by the organic carbon partitioning coefficient (K\u003csub\u003eoc\u003c/sub\u003e) of the comound and its respective fraction quantified in samples (f\u003csub\u003eoc\u003c/sub\u003e) (MEC\u0026thinsp;=\u0026thinsp;C\u003csub\u003esed\u003c/sub\u003e/(K\u003csub\u003eoc\u003c/sub\u003e x f\u003csub\u003eoc\u003c/sub\u003e). The risk assessment consisted of ratios between the measured environmental concentrations of each hormone and their respective PNEC (RQ\u0026thinsp;=\u0026thinsp;MEC/PNEC). followed by the risk characterization as follows: low for RQ\u0026thinsp;\u0026lt;\u0026thinsp;0.1), medium for RQ between 0.1 and 1(0.1\u0026thinsp;\u0026lt;\u0026thinsp;RQ\u0026thinsp;\u0026lt;\u0026thinsp;1), and high for RQ above 1 (RQ\u0026thinsp;\u0026ge;\u0026thinsp;1) (Blair et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cstrong\u003eSterols determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe individual and total concentrations (\u0026sum;sterols) of the analyzed sterols are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and in Supplementary Information (Table 4). All sterols were detected at all stations, and the \u0026sum;sterols ranged from 271.5 to 2525 ng g\u003csup\u003e-1\u003c/sup\u003e. The sterol with the highest concentration was \u0026beta;-SITO, especially in the estuarine region (ACR8 to ACR10), which is plausible because mangrove plants are the main source of this natural sterol (Santos et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Volkman et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The levels of ERG, followed by COL, were also high, which must be attributed to most of the points being in rural areas. As reported by Weete (\u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e), the presence of ERG levels is due to fungal decomposition, while the COL comes from various sources, such as some types of marine algae organisms and animal lipids. However, it may be related to human and animal feces and the dealkylation of C28 and C29 phytosterols promoted by zooplankton species in areas far from the coast (Volkman, 1986; Prost et al., 2017).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIndividual and total sterol concentrations and diagnostic ratios in surface sediments of the Acara\u0026uacute; River basin (ng g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompounds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR01\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR02\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR03\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR04\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR05\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR06\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR07\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR08\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR09\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACR10\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\u003eSterols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e754.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e241.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e438.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHOLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e442.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e431.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e567.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e885.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;-SITO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e386.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e714.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e766.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e679.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Sigma;Sterols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2005.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1345.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e271.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e559.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e576.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e669.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1563.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1361.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1587.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2525.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCop/Col\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCop/(Cop\u0026thinsp;+\u0026thinsp;Choln)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCop/Choln\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCop/(Choln\u0026thinsp;+\u0026thinsp;Col)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholn/Col\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eCOP is the most abundant sterol in human feces and is due to the fact that they are biosynthesized from the conversion of CHOL by bacterial reduction in the intestine of higher animals, and therefore are considered the main indicator of sewage input into the environment (Leeming and Nichols, 1996). The concentration of COP ranging from 6 to 124.1 ng g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in Acara\u0026uacute; River Basin showed concentrations closest to less inhabited areas in Brazil as Bitupit\u0026aacute;, S\u0026atilde;o Caetano de Odivelas, Barra de S\u0026atilde;o Miguel and lower than values found in more developed cities or estuaries with any industrial activity as Rio de Janeiro, Cubat\u0026atilde;o, Macei\u0026oacute;, Natal and Florian\u0026oacute;polis and other (Cordeiro et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Campos et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Abreu-Mota et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Martins et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ara\u0026uacute;jo et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Santos et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The concentration of COP represented 3.1% of the \u0026Sigma;sterols, showing low contamination by sewage and possibly associated with the presence of input from rural activities (Santos et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several studies use diagnostic ratios to identify and distinguish inputs from multiple sources as well as assessment the influence of anthropogenic activities on the development of the region (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiagnostic ratios used for samples from the Acara\u0026uacute; River Basin.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiagnostic ratios\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThreshold levels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnvironment status\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\u003eRatio 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u0026ndash;1\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSewage-contaminated\u003c/p\u003e\n \u003cp\u003eHighly contaminated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.4\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUncontaminated\u003c/p\u003e\n \u003cp\u003eFecal contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.3\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUncontaminated\u003c/p\u003e\n \u003cp\u003eSewage-contaminated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio 4\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.15\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUncontaminated\u003c/p\u003e\n \u003cp\u003eFecal contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio 5\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFresh organic matter input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOP. \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;100 ng.g\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;500 ng.g\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUncontaminated\u003c/p\u003e\n \u003cp\u003eSewage-contaminated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003eCoprostanol/cholesterol ratio (Takada et al., 1994)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003eb\u003c/sup\u003eCoprostanol/cholestanol ratio (Shah et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003ec\u003c/sup\u003eCoprostanol/(coprostanol\u0026thinsp;+\u0026thinsp;cholestanol) ratio (Grimalt et al., \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003ed\u003c/sup\u003eCoprostanol/(cholestanol\u0026thinsp;+\u0026thinsp;cholesterol) ratio (Chan et al., \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003ee\u003c/sup\u003eColestanol/cholesterol ratio (Canuel and Martens, \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003ef\u003c/sup\u003eCoprostanol levels (Gonz\u0026aacute;lez-Oreja and Saiz-Salinas, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e)\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\u003eAccording to Takada et al. (1994), the Ratio 1 values greater than 1 indicate highly contaminated sites, while values ranging from 0.2 to 1 indicate sites contaminated with sewage. As can be seen in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the Ratio 1 ranged from 0.05 to 0.30, characterizing the areas studied as being sewage-contaminated. The Ratio 2 values\u0026thinsp;\u0026gt;\u0026thinsp;0.4 indicate human fecal contamination (Shah et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), and as can be seen in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, it ranged from 0.17 to 0.86, a characteristic of fecal contamination in most of the environments studied (ACR01, ACR04, ACR05, ACR08, ACR09 and ACR010). In the Ratio 3 values\u0026thinsp;\u0026lt;\u0026thinsp;0.3 characterize uncontaminated areas, while values\u0026thinsp;\u0026gt;\u0026thinsp;0.7 characterize sewage-contaminated (Grimalt et al., \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e). In Acara\u0026uacute; river the characterization of the contamination was not homogeneous, the places characterized as contaminated were ACR04, ACR05, ACR08 and ACR09, and others not contaminated only sites 6 and 7, while four places cannot be characterized. According to Abreu-Mota et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), the reduced form of COL in the environment is CHOLN indicating inconclusive input. In the Ratio 4\u0026thinsp;\u0026lt;\u0026thinsp;0.15 indicates an uncontaminated site, while\u0026thinsp;\u0026gt;\u0026thinsp;0.20 indicates contamination by fecal matter (Chan et al., \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). The Ratio 4 indicated a site uncontaminated by sewage in most of the sites studied (ACR01 toACR07), while in two locations it shows highly contaminated (ACR09 andACR10). According to Canuel and Martens (\u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e) the Ratio 5 values\u0026thinsp;\u0026lt;\u0026thinsp;0.5 indicate fresh organic matter input, while ranged from 0.06 to 0.43 is indicative from unsaturated form with respect to its saturated homologue.\u003c/p\u003e\n\u003cp\u003eCOP levels higher than 500 ng g-1 is considered as an indicator of sewage contamination, while levels less than 100 ng g-1 are considered indicative of a natural site (Writer et al., \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e; Gonz\u0026aacute;lez-Oreja and Saiz-Salinas, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). None of the stations showed concentrations larger than this value, except station ACR10 which certainly receives influence from sewage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEDCs determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNatural and synthetic EDCs were detected at all stations (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSupplementary Information, Table\u0026nbsp;5\u003c/span\u003e). The average total concentrations (\u0026sum;nat. hormones) was 21.78 ng g\u003csup\u003e-1\u003c/sup\u003e, while \u0026sum;synt. hormones was 117.45 ng g\u003csup\u003e-1\u003c/sup\u003e and were quantified in higher concentrations than \u0026sum;nat. hormones at all sample sites.\u003c/p\u003e\n\u003cp\u003eThe higher values detected for synthetic hormones may be related to their lower water solubility and higher log Kow, preferentially partitioning to sediment than to water (Caldwell et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Matić et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).In general, levels of hormones are usually larger in estuaries from Brazil than in other countries which can be explained due to the lack of waste treatment (Arditsoglou and Voutsa, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Froehner et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gorga et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Isobe et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Morais et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pimentel et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Santos et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to Andaluri et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) estimative indicate that the global human population contributes approximately 31,000 kg year\u003csup\u003e-1\u003c/sup\u003e of natural steroid estrogens, while only the European Union and the USA are responsible for an annual discharge of estrogen by the livestock sector of approximately 84,000 kg year\u003csup\u003e-1\u003c/sup\u003e, more than two times higher than the human discharge rate (Adeel et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Another cause can be the number of compounds examined in each study, provoking the higher presence of natural hormones in comparison with synthetic hormones, usually represented by one or two compounds (Lei et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Another important point is the fact that the synthetic EDCs studied have high log Kow ranging from 4.1 to 5.6 compared to natural EDCs (log Kow 2.8 to 3.9), and therefore the environmental partition is expected to be preferentially for the sediment compartment, especially those sediments rich in organic matter (Lei et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Caldwell et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e17\u0026alpha;-E2 (nd \u0026minus;\u0026thinsp;208.4 ng g\u003csup\u003e-1\u003c/sup\u003e), 17\u0026beta;-E2 (nd \u0026minus;\u0026thinsp;29.5 ng g\u003csup\u003e-1\u003c/sup\u003e), E1 (nd \u0026minus;\u0026thinsp;73.83 ng g\u003csup\u003e-1\u003c/sup\u003e) and E3 (0.07\u0026ndash;25.41 ng g\u003csup\u003e-1\u003c/sup\u003e) concentrations were 5.4%, 1.2%, 5.4% and 2% of \u0026sum;hormones, respectively, and \u0026sum;nat. hormones were responsible for 14% of patients. These hormones are produced and excreted by humans and animals (Ternes et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), and are easily degraded by bacteria at sites with abundant nutrients (Lintelmann et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Estriol was the only natural hormone present in all stations. This chemical is also used in clinical treatments (Ali et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and swine farms (Adeel et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and is classified as harmful to the environment (Carlsson et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The values found in the Acara\u0026uacute; River Basin are much lower than those in estuaries with high industrial activity and larger populations (Pusceddu et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), but still higher than those in other countries (Isobe et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Arditsoglou and Voutsa, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMestranol and 17\u0026alpha;ethinylestradiol ranged from 2.77 to 251.7 ng g\u003csup\u003e-1\u003c/sup\u003e and from 51.2 to 153.8 ng g\u003csup\u003e-1\u003c/sup\u003e, respectively. Dienestrol ranged from nd to 50.5 ng g\u003csup\u003e-1\u003c/sup\u003e and was the least abundant synthetic hormone detected. Diethylstilbestrol (DES) was the compound with the highest representativeness among all the hormones (46% of \u0026sum;hormones), ranging from 3.29 to 1474 ng g\u003csup\u003e-1\u003c/sup\u003e. DES was first prescribed to pregnant women in the 1940s to prevent complications in pregnancy and later to prevent menopausal symptoms (Jama, \u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e). in the 70s was found to cause adverse effects such as cancer (Herbst et al., \u003cspan class=\"CitationRef\"\u003e1971\u003c/span\u003e). DES is still used in livestock and aquaculture, which is a means of contaminating natural environments and food (Adedeji et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yang et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wei et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental distribution and sources of sterols and EDCs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Oliveira et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), few studies have investigated the effects of sediment components on the destination and distribution of organic contaminants, which should contribute significantly to explaining the patterns of environmental and geographic distribution. According to Luthy et al. (\u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), there is consensus that the accumulation of organic contaminants in sediments is governed by sorption on amorphous organic matter (e.g., fulvic and humic acids, lignin, proteins, and others) and carbonaceous materials (e.g., coal, black coal, and kerogen) (Cornelissen et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the correlations among sterols, EDCs, and inorganic and organic sediment composition parameters. The \u0026sum;sterols and the majority of the individual sterols were moderate (0.4 to 0.59) to strong (0.60 to 0.79) (Evans,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e), and were significantly correlated with the fine-grained sediments (silt or clay) and the organic parameters (TOC, HA/TOC, and FA/TOC).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpearman\u0026apos;s rank correlation coefficient (R2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for the dependence of compounds and sediment components.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGrain size fractions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOrganic parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSand (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSilt (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClay (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTOC (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHA (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFA (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBC (%)\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\u003eDES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeEE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17-\u0026alpha;EE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026alpha;-E2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026beta;-E2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;\u003csub\u003eSint. Hormones\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;\u003csub\u003eNat. Hormones\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;\u003csub\u003eHormones\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHOLESTAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESTIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-SITO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;sterols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe COP presented the highest positive correlation with fine-grained sediments and associated organic parameters. Among the sterols, only COL showed no significant correlation with the inorganic or organic sediment parameters. Several authors have indicated that fecal and phytosterols are highly hydrophobic, are mainly adsorbed and transported by fine suspended particles, and accumulate in sediments with high levels of TOC (Tolosa et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Speranza et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Souza et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Santos et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gomes et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, considering that the analyzed sterols presented specific sources of organic matter (aquatic, terrestrial, and anthropogenic), the positive and significant correlations suggest that the distribution of fine-grained sediments and their organic parameters are decisive factors driving the accumulation of sterols, especially the COP, along the Acara\u0026uacute; River.\u003c/p\u003e\n\u003cp\u003eOf all the analyzed EDCs, only E1 presented a significant correlation with the inorganic and organic parameters of the sediment. The absence of a correlation between the majority of the estrogenic hormones and the sediment composition parameters is because these compound distributions are more related to the anthropogenic input from point sources of pollution than the sediment characteristics. Black carbon (BC) is a known natural adsorbent in aquatic environments; however, only DES showed a significant correlation. BC is likely to originate from urban activities, such as fossil fuel use and biomass as energy sources in productive activities (e.g., bakeries, steakhouses, and brickworks), whereas in rural regions, the contribution comes from fires such as land management, as well as burning wood and coal as a source of energy in production processes (e.g., manufacture of red ceramics) and residential areas (Cavalcante et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Santos et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSeveral studies show low or no correlation of organic contaminants with BC in terms of environmental distribution and this is due to BC being linked to urban and rural sources throughout the geographic range (Oliveira et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gama et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Andrade et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, the studied contaminants have their inputs at specific points linked to urban or rural sources separately.\u003c/p\u003e\n\u003cp\u003eTo understand the influence of the various anthropogenic activities in the region, we applied an integrated environmental assessment using sterol and EDCs levels to provide a more faithful idea of the situation of the environment. Therefore, the relative contribution of the main activities was quantified by source apportionment using PCA with land-use classification (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The first component (PC1) accounted for 35.5% and the second component (PC2) accounted for 30.7% of the total variance (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). PC1 separated stations ACR01, ACR07, ACR08, ACR09, and ACR10 from stations ACR02, ACR03, ACR04, ACR05, and ACR06, whereas PC2 separated stations ACR01, ACR02, ACR04, ACR05, ACR08, and ACR09 from stations ACR03, ACR06, ACR07, and ACR10 (Supplementary Information Fig. 5).\u003c/p\u003e\n\u003cp\u003ePC1 performed with diagnostic ratios of steroids and concentrations of natural and synthetic EDCs grouped stations of a greater fluvial nature than those considered coastal (except for ACR01). The PC1was positively loaded with all chemical substances used as markers. Therefore, PC1 positive values can be associated with a mixture of natural environment and pollution by input of waste from rural and urban activities at stations ACR01, ACR07, and ACR10. In relation to PC2 it was negatively loaded by Ratio 1 to Ratio 4, as well by DES, DIE, E1 and COP, and thus, negative values for PC2 also indicate mixture of pollution at stations ACR01, ACR02, ACR04, ACR05, ACR08 and ACR09. On the other hand, was positively loaded by MeEE2, 17-\u0026alpha;EE2, 17\u0026alpha;-E2, 17\u0026beta;-E2, and E3 on ACR03, ACR06, ACR07, and ACR10 also showed a mixture of sources.\u003c/p\u003e\n\u003cp\u003ePC1 explained the environmental condition in relation to the input related to rural activities, especially those using drugs in animal management, because it was responsible for the largest total variances, and also because it grouped stations ACR01, ACR07 to ACR10. The ACR01 station is right at the exit of A\u0026ccedil;ude Varjota (water reservoir), and since the 1990s projects for the use of fish farming have been encouraged in these water bodies (Sousa at al., 2016), while regarding points ACR07 to ACR10, shrimp farms are the main economic activities in this region.\u003c/p\u003e\n\u003cp\u003eThe impacts of rural activities, which have been less studied as sources of pollution, are increasingly emerging. DES detected in sewage in China has been attributed to the overuse of hormones and/or their illegal use in animal feed (Jin et al. 2008). The highest levels of natural hormones were found in areas influenced by cities, whereas synthetic hormones were found only in areas influenced by rural-urban inputs (Liu et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Rural activities, especially fish and shrimp farming and livestock, are responsible for the largest contribution of synthetic EDCs in the environment (e.g., DIE, DES, \u0026alpha;EE2, and MeEE2) and natural hormones (e.g., \u0026alpha;E2, E1, and \u0026beta;E2) in tropical mangroves with a restricted predominance of human sewage (Santos et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The levels of synthetic EDCs used as contraceptives began to be recorded in a sediment core in one of the largest rivers in the Brazilian semi-arid region (Rio Jaguaribe) since the 60s, while the hormones used in animal management after the 80s, which was attributed to the development of rural activities such as livestock, shrimp, and fish farming (Santos et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, the Acara\u0026uacute; region does not have an economic culture based on livestock; therefore, the data indicate that the main economic activities, such as fish and shrimp farming that use drugs in animal husbandry, are certainly the predominant anthropic sources of EDCs in the region, even surpassing the input from classic urban activities, such as chronic release of treated or untreated sewage. The data also show areas of the river that are only impacted by urban activity, such as the release of untreated sewage or inefficient treatment systems, and areas that are impacted by both urban and rural activities, such as fish and shrimp farming, which are predominant in the region.\u003c/p\u003e\n\u003cp\u003eOn the one hand, the stations located in ACR04 and ACR08 to ACR10 are the most populous cities; therefore, the levels of markers linked to sewage are justified due to the insufficient structure of the basic sanitation systems of these cities (\u0026lt;\u0026thinsp;50%, IBGE). The rest of the stations ACR02, ACR03, ACR04, and ACR06 are sparsely populated cities, and do not have developed agricultural activities, and thus contribute to the environment only with markers linked to urban activity, especially because of the fragile structure of basic sanitation systems (5 to 20%, IBGE 2023). According to Morais et al. (2018), the evidence that sterols, especially those linked to fecal input, not only come from cities with insufficient or no sewage treatment, but COP levels in the final effluent, depending on the treatment time in the sewage treatment plants, can be in the magnitudes of the first to the eighth most abundant sterol (Neur et al., 1994; Reichwaldt et al., 2016) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we performed a risk assessment based on RQ calculations for the contamination levels of hormones quantified in sediment samples from the Acara\u0026uacute; River, since this group of substances is classified as EDC. For each compound, the compilation of waterborne toxicity data is detailed in the appendix along with the parameters for the RQ calculations (Supplementary Information Tables 6 to 11 and Fig. 6). The RQ results showed that all samples produced a low risk of adverse effects for diethylstilbestrol, estriol, and 17\u0026alpha;-estradiol. The concentrations of dienestrol (ACR-03, ACR-07, and ACR-08), 17\u0026beta;-estradiol (ACR-07), and estrone (ACR-03 and ACR-09) showed medium risk. A high risk of adverse effects (RQ\u0026thinsp;\u0026ge;\u0026thinsp;1) was observed in all samples for 17\u0026alpha;-ethinylestradiol, mestranol and estrone (except ACR-03 and ACR-09), in addition to dienestrol (ACR-01 and ACR-09) and 17\u0026alpha;-Estradiol (ACR-01 and ACR-07) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Information Tables 12 and 13).\u003c/p\u003e\n\u003cp\u003eIt is important to highlight that the ERA was based on liquid-phase toxicity due to the limited number of studies targeting the sediment toxicity of all hormones. However, the results indicate that 17\u0026alpha;-ethinylestradiol, mestranol and estrone are compounds of environmental concern in the Acar\u0026uacute; River, which is consistent with the literature regarding endocrine-disrupting substances in the environment. For instance, the issue of feminized fish has been related to the presence of estrogens in the aquatic ecosystem, particularly the steroids 17\u0026beta;-estradiol and 17\u0026alpha;-ethinylestradiol as the main compounds, along with the natural hormone estrone, which potentially contributes to estrogenicity (Ankley et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Pimentel et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), the studied region already has examples of evidence of fish feminization attributed to high levels of EDCs.\u003c/p\u003e\n\u003cp\u003eEstrogenic compounds can bind to estrogen receptors (ER) at the molecular level, triggering an adverse outcome pathway that can alter reproduction at the whole-organism level, with consequent effects on populations. In such a process, other biological responses may occur following ER activation, including gene expression alterations, variations in sex steroid and vitellogenin levels (e.g., egg yolk protein), gonad deformities, alterations in secondary sex characteristics, and reproductive behavior (Ankley et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn addition to the effects of the estrogen signaling pathway, non-specific responses have been reported for 17\u0026alpha;-ethinylestradiol and estrone in the sediments. Bioaccumulation of the 17a-ethinylestradiol in \u003cem\u003eChironomus dilutus\u003c/em\u003e and \u003cem\u003eHyalella azteca\u003c/em\u003e was reported in exposures with freshwater spiked samples (Dussault et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), indicating a potential uptake of the compound by benthic organisms, but no toxicity on Survival and growth was induced in both species at 1.2 to 7.6 mg Kg\u003csup\u003e-1\u003c/sup\u003e (Gilroy et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn marine sediments spiked with 0.01 to 100 ng g\u003csup\u003e-1\u003c/sup\u003e of 17a-ethinylestradiol, no toxicity was reported for the amphipod \u003cem\u003eAmpelisca brevicornis\u003c/em\u003e, but significant responses were observed on the bioluminescence inhibition of the bacteria \u003cem\u003eVibrio fischeri\u003c/em\u003e (IC50 of 39.4 ng g\u003csup\u003e-1\u003c/sup\u003e) in solid phase experiments (Maranho et al. \u003cspan class=\"CitationRef\"\u003e2015a\u003c/span\u003e). In the same study, elutriates produced spermiotoxicity and embryotoxicity to the sea urchin \u003cem\u003eParacentrotus lividus\u003c/em\u003e (LOEC of 0.01 ng g\u003csup\u003e-1\u003c/sup\u003e), and impaired growth rate of the microalgae \u003cem\u003eIsochrysis galbana\u003c/em\u003e and \u003cem\u003eTetraselmis chuii\u003c/em\u003e at 1 ng g\u003csup\u003e-1\u003c/sup\u003e. At the sublethal level, the energy status of the polychaete \u003cem\u003eHediste diversicolor\u003c/em\u003e was not affected by 17\u0026alpha;-ethinylestradiol, but the activities of monoamine oxidase (neuroendocrine response) and cyclooxygenase (inflammatory process mediator) were altered following the 14-day exposure of sediment spiked with concentrations from 0.01 to 100 ng g\u003csup\u003e-1\u003c/sup\u003e (Maranho et al. \u003cspan class=\"CitationRef\"\u003e2015b\u003c/span\u003e). Thus, we consider that the inefficient sewage treatment system combined with rural activities releases hormones to sediments, impairing their quality and posing risks to benthic organisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy using an anthropogenic molecular marker approach, we demonstrated that land use conditions in a semi-arid estuary resulted in the input of chemicals into the system related to urban and rural activities. In some areas, the absence of an efficient sewage treatment system has contributed to the impact along the Acar\u0026uacute; River. On the other hand, the study showed that in regions of the lower estuary that have intense rural activity, the main sources of synthetic EDC are linked to fish and shrimp farming, surpassing even the contribution from sewage effluents. The data showed that 17α-ethinylestradiol, mestranol, and estrone are compounds of environmental concern in the Acar\u0026uacute; River, and this is an important baseline for further assessment, pollution control policies, and conservation decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors were involved in the conception and design of the study and reviewed the final manuscript version. R.M.C. is the Principal Investigator responsible for the Conceptualization, Investigation, Project administration, visualization, and writing of the original draft. A.D.S.P., M.F.B.L., E.V.M., P.M., G.M.F., R.P.S., R.F.N., and F.R.S. participated in the conceptualization, investigation, and formal analysis, and wrote the original draft. L.B.M. participated in the investigation, visualization, and formal analysis, wrote the manuscript, and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded by the Brazilian National Council for Scientific and Technological Development (CNPq), the Financier of Studies and Projects (FINEP), the Cear\u0026aacute; Foundation for Support to Scientific and Technological Development (FUNCAP), and the S\u0026atilde;o Paulo Research Foundation (FAPESP). R.M. Cavalcante is grateful for the PQ-2 (Grant number 315281/2020-0-CNPq), as well as Lemae/Finep/CNPq (Grant number 380975/2022\u0026ndash;0), Pronen/FUNCAP/CNPq (Grant number Pne-0112-00007.01.00/16) and Pronex (Grant number PR2 0101 00008 01 00/15). The authors thank the members of LACOr for their enthusiasm and energy related to scientific work. \u0026ldquo;essas pessoas s\u0026atilde;o as melhores\u0026rdquo;. L.B. 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Levels of estrogenic compounds in Xiamen Bay sediment, China. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e 58, 1210\u0026ndash;1216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.marpolbul.2009.03.011\u003c/span\u003e\u003cspan address=\"10.1016/j.marpolbul.2009.03.011\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fecal sterols, endocrine disrupting compounds, estrogen hormones, sewage pollution, source apportionment, ecological risk","lastPublishedDoi":"10.21203/rs.3.rs-7054890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7054890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSemiarid regions are unique, and in Northeast Brazil, estuarine basins are often impacted by human occupation, resulting in the input of chemicals. The objective of this study was to evaluate the contributions of urban and rural activities to the environmental quality of the Acara\u0026uacute; River, using molecular markers in superficial sediments. Concentrations of total sterols and hormones varied between 271.5 to 2525 ng g\u003csup\u003e-1\u003c/sup\u003e and 139.8 to 1728 ng g\u003csup\u003e-1\u003c/sup\u003e, respectively. Fecal sterol coprostanol ranged from 6 to 124.1 ng g\u003csup\u003e-1\u003c/sup\u003e. Concentrations of synthetic hormones were detected at one order of magnitude higher than those of natural hormones, and the diagnostic ratios for sterols, hormones, and coprostanol suggest sewage discharge and fecal contamination in the Araca\u0026uacute; River Basin. Activities such as fish and shrimp farming, which involve the use of drugs for animal handling, may also be relevant sources in the region. Regarding the ecological risks of toxicity, 17α-ethinylestradiol, mestranol, and estrone are compounds of environmental concern in the Acar\u0026uacute; River, requiring actions to reduce or eliminate their sources.\u003c/p\u003e","manuscriptTitle":"Impacts of rural and urban sources on a tropical semiarid region (Acaraú River, Ceará, Brazil): sedimentary sterols and endocrine-disrupting compounds as anthropogenic molecular markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 13:08:00","doi":"10.21203/rs.3.rs-7054890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-15T11:30:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T15:54:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T04:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16763145464064033892395050194162611746","date":"2025-07-18T15:00:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243999082229502199428506145651647104164","date":"2025-07-18T01:21:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70093844689794925642432736774499386941","date":"2025-07-15T13:44:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-13T10:22:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-10T11:25:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T17:45:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Geochemistry and Health","date":"2025-07-05T20:27:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"28382c72-a1bd-4ae8-aaf4-9ea70e4e3506","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T15:59:20+00:00","versionOfRecord":{"articleIdentity":"rs-7054890","link":"https://doi.org/10.1007/s10653-025-02742-z","journal":{"identity":"environmental-geochemistry-and-health","isVorOnly":false,"title":"Environmental Geochemistry and Health"},"publishedOn":"2025-08-31 15:56:50","publishedOnDateReadable":"August 31st, 2025"},"versionCreatedAt":"2025-07-15 13:08:00","video":"","vorDoi":"10.1007/s10653-025-02742-z","vorDoiUrl":"https://doi.org/10.1007/s10653-025-02742-z","workflowStages":[]},"version":"v1","identity":"rs-7054890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7054890","identity":"rs-7054890","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}