Overview of systematic monitoring networks for surface water quality in the Amazon River Basin

Overview of systematic monitoring networks for surface water quality in the Amazon River Basin | 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 Overview of systematic monitoring networks for surface water quality in the Amazon River Basin Luanna Costa Dias, Luiza Carla Girard Mendes Teixeira, Lindemberg Lima Fernandes, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8602785/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Water quality monitoring is essential for water resource management, especially when carried out systematically (continuously, standardized, and with data available), where it is possible to statistically analyze each parameter and generate a historical series. The Amazon River basin is important for biodiversity and climate regulation and is home to a variety of economic activities, which makes monitoring extremely valuable for decision-making. Currently, the Amazon River basin has four systematic water quality monitoring networks: the National Hydrometeorological Network (RHN), the Surface Water Quality Monitoring Network (RNQA), the Hidrosat virtual network, and the Amazonas Water, Air, and Soil Quality Monitoring Program (ProQAS/AM). The RHN is the oldest, with the best spatial distribution and the fewest parameters measured. Hidrosat only estimates the parameter of suspended sediment. ProQAS/AM is systematic only in Manaus-AM. Finally, the RNQA is a recent network established by ANA and coordinated by the states to generate continuous water quality information, but it has concentrated points that leave geographical gaps. Based on the overview presented, it is possible to conduct numerous studies with data from the systematic networks and even evaluate the efficiency of each initiative's operation, in order to obtain increasingly reliable and continuous data. monitoring point historical series systematic monitoring Amazon water management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. INTRODUCTION Surface water is the most important and accessible source of water for human life and agro-industrial production. However, because it is easy to collect, it is the most polluted in many countries (Aboutalebi et al. 2016; Jiang et al. 2020). Given this, monitoring water quality is essential for water resource management, as it is important for understanding and protecting aquatic environments, elucidating the processes that affect water quality, and detecting and analyzing spatial and temporal trends. The topic of design and optimization of water quality monitoring networks is frequently studied around the world. The first monitoring networks appeared in the 1960s (Sanders et al. 1983), but according to Chapman and Kimstach (1992), modern monitoring began in the 1950s with a focus solely on data collection. Sharp (1971) conducted one of the first studies in the literature to optimize water quality monitoring points, with the objective of locating sources of pollution. For classic authors on this topic (Sharp 1971; Sanders et al., 1983; Chapman and Kimstach 1992; Harmancioglu et al. 1999), the definition of water quality monitoring is to obtain physical, chemical, and biological control of water characteristics using statistical sampling. These works highlight the complexity of monitoring, the need to optimize the network, and emphasize the particularities and difficulties for developing countries, as mentioned by Gradilla-Hernández et al. (2022): “in countries in the southern hemisphere, water quality monitoring networks are inefficient due to the use of subjective strategies and insufficient investment”. Even with the limitations of these studies, as they date from the late 20th century, there was already concern about disseminating procedures and methodologies for optimizing the water quality monitoring network. Since the 2000s, there have been a large number of articles on a wide variety of network optimization methods, such as: the use of entropy (Karaman 2013), the application of hydrodynamics to contaminant transport (Telci et al. 2009), use of multivariate and cluster analysis (Guigues et al. 2013), “VIKOR” multi-criteria decision making (Chang and Lin 2013), genetic algorithms for network optimization (Liyanage et al. 2016; Kyna Borel et al. 2017;), Bayesian method (Destandau and Zaiter 2020), and geostatistical techniques of Kendall's W (Camara et al. 2020). According to Sharp (1971), the three stages of network design are: selection of indicator parameters, definition of sampling locations, and determination of collection frequency. In addition, the specific objectives of monitoring, easy access to sampling points, representativeness in the watershed, surveillance of pollution sources, estimation of pollutant loads, and water use must be considered (Aboutalebi et al. 2016). Based on Destandau and Zaiter (2020), there are two types of networks: surveillance control networks and operational control networks (which are temporary). The surveillance control network is like the monitoring networks operated by government agencies to comply with their environmental legislation objectives. This type of network is also known as systematic monitoring, which, according to Libos et al. (2022), consists of fixed sampling points with defined parameters, methodologies, and frequencies. The operational control network, on the other hand, corresponds to the self-monitoring of potentially polluting companies, which is also called non-systematic monitoring, as it is not standardized and has a random frequency. In Brazil, water monitoring networks were established based on state monitoring, without any standardization, with the first Brazilian networks beginning in the 1970s (ANA 2012). In 1974, CETESB (São Paulo State Environmental Company) began monitoring the water quality of rivers and reservoirs to control pollution, and adapted the Water Quality Index (WQI), which is widely used in Brazil (Medeiros 2012). Until 2014, Brazil's surface water quality monitoring networks were not standardized, with only 17 Brazilian states monitoring water quality systematically. It was only with the creation of the Surface Water Quality Monitoring Network (RNQA) by the National Water and Basic Sanitation Agency (ANA) in 2013 that guidelines for operation and systematic monitoring were established (ANA 2012). In 2016, all states in the Northern Region of Brazil, with the exception of Tocantins, did not have a state monitoring network (ANA 2022), which means that the rivers in these states, most of which are part of the Amazon River basin, lack adequate knowledge of water quality in the world's largest hydrographic basin. Given this scenario, the objective of this study is to assess the current situation of surface water quality monitoring networks in the Amazon River basin through a spatiotemporal analysis of existing points, measured parameters, and data frequency. Thus, an overview of surface water quality monitoring in an area of global relevance is presented considering the current climate change scenario, which tends to affect water quality for its various uses. 2. STUDY AREA The division of Brazilian territory into river basins was carried out in 1972 by the old National Department of Water and Electric Power (DNAEE), with the objective of implementing an information system and codifying the fluviometric stations that make up the National Hydrometeorological Network (RHN) (ANA 2021). In this division, there are nine hydrographic basins, each of which is subdivided into ten sub-basins, Fig. 01 (a). Basin 1 corresponds to the Amazon River, which is the study area, as shown in Fig. 01 (b). The Amazon River basin covers approximately 6.3 million km² in Brazil and includes the states of Acre, Amazonas, Rondônia, and Roraima, as well as parts of the states of Amapá, Mato Grosso, and Pará (Dias et al. 2022). It has a population of approximately 12 million inhabitants, which corresponds to about 5% of Brazil's population, with the most populous urban centers being Manaus (2 million inhabitants), Porto Velho (517,000 inhabitants), and Rio Branco (387,000 inhabitants) (IBGE 2023). According to ANA (2012) and OTCA (2023), the main pressures on water quality in the Amazon basin are: domestic sewage and solid waste, industrial activities such as the Manaus Free Trade Zone, mining and prospecting in the Tapajós and Madeira basins, deforestation and inadequate soil management (arc of deforestation in the south of the basin), hydroelectric exploitation, navigation (which is the only means of transport in many places), and the effects of droughts such as those of 2005, 2010, 2023, and 2024. The natural conditions of the waters of the Amazon River basin are determined by the geology and vegetation that establish physical and chemical characteristics (ANA 2012). In the Amazon basin, there are important areas of sedimentation originating in the Andes (Brigel and Gutierrez, 2024). The colors they take on were classified by Sioli ( 1984 ) using geological formation, water color, sediment load, electrical conductivity, and humus formation (organic matter), which is directly related to a decrease in pH. Based on this, they were classified as white waters (neutral pH between 6.2–7.2), black waters (low pH of 3.8–4.9), and clear waters (pH between the extremes of white and black waters of 4.5–7.8) (Rudorff et al. 2011 ). White waters are highly turbid, muddy in color, originate in the Andes, and are rich in mineral salts and suspended matter, such as those of the Solimões, Madeira, Juruá, and Purus rivers. Black waters, on the other hand, are dark because they drain areas of plains and forests with soils rich in organic matter, such as the Negro River. Clear waters are greenish or transparent because they drain waters from crystalline areas such as ancient rocks, such as those of the Tapajós River (Sioli 1984 ; Brigel and Gutierrez 2024; Duvoisin Jr. et al. 2025 ). The main rivers in the Amazon River basin on the right margin are the Javari, Jutaí, Juruá, Purus, Madeira, Tapajós, and Xingu rivers. Those on the left margin are the Japurá, Negro, Trombetas, Jari, and Nhamundá rivers. The mean streamflow of the Amazon River is 132,145 m³/s and has a regular hydrological regime, with well-defined dry and wet seasons (ANA 2012; Coutinho et al. 2019 ). The rainfall regime varies across its different regions, with average precipitation ranging from 2,000 to 2,500 mm/year (Lira 2019 ; Fisch et al. 1998 ; Ishihara et al. 2014 ; Molinier et al. 1995 ; Souza et al. 2016). 3. MATERIALS AND METHODS To achieve the objectives of the study, we first surveyed the information available at ANA, state secretariats, and public institutions (such as universities and environmental entities) on networks that systematically monitor water quality. The criteria adopted for data to be considered systematic is based on the concept presented by Destandau and Zaiter ( 2020 ) and Libos et al. (2022), in which they consider monitoring points to be those that are spatialized, standardized, continuous over time, and with information available to the public through GIS (Geographic Information System). Given these requirements, the monitoring network is considered systematic (also classified as a surveillance control network by Destandau and Zaiter 2020 ). Figure 02 illustrates the flow of this stage. After detecting the systematic networks, the information was recorded in spreadsheets and the available historical series were downloaded up to the year 2024 within the limits of the Amazon River basin according to the DNAEE classification (Fig. 01 b). In addition, the data series for each station was analyzed in order to identify which parameters are monitored, the operators, the frequency, failures, and gaps in the historical series. This is important to understand whether systematic monitoring has consistent information and is in fact effective. The stations of each systematic network were spatialized using QGis version 3.28 in order to analyze the geographical distribution within the study area and its respective coverage. According to Resolution No. 903 (ANA, 2013), which created the RNQA, three regions were defined for implementation, with this study focusing on Region 1, which must have at least one monitoring point per 10,000 km². Based on this, the density of points in relation to the area of each of the 10 sub-basins (Fig. 01 b) was determined to verify compliance with the proposed minimum density of points. The methodological steps are intended to assess whether the network is indeed systematic (Figure 02), the quality of the available data, and its spatial and temporal distribution. This provides an overview of what surface water quality data is available and where to find it. 4. RESULTS AND DISCUSSION Systematic monitoring of surface water quality in Brazil was conceived through state initiatives in which each state adopted its own criteria for monitoring. In the 1990s, with the advent of the National Water Resources Policy (BRAZIL, 1997), which instituted decentralized and participatory management and has as one of its objectives “to ensure the availability of water at adequate quality standards,” the focus began to shift toward the creation of a national water quality monitoring network. According to ANA (2012), water quality monitoring in Brazil began in the 1970s, through the measurement of some basic parameters at RHN fluviometric points and by sanitation companies with a focus on potability, such as CETESB. According to Technical Note No. 62/2023/SGH from ANA (2023), the first water quality monitoring network began in 1990 with its implementation in the Rio Doce basin. From then on, a specific network was designed to monitor water quality, rather than relying on the RHN's fluviometric stations. The only initiative to improve these networks took place in 2008 with the launch of the National Water Quality Assessment Program (PNQA), whose main objective was to provide society with adequate knowledge about the quality of Brazilian surface waters. One of the components of the PNQA is the Surface Water Quality Monitoring Network (RNQA), which aims to expand and optimize the geographic distribution of water quality data (ANA, 2023). The RNQA is published in ANA Resolution No. 903 (ANA, 2013), and states participate on a voluntary basis. To encourage states and strengthen the RNQA, in 2014, through Resolution No. 1,040, which was replaced by Resolution No. 643 (ANA, 2016), the Program to Encourage the Disclosure of Water Quality Data (Qualiágua) was launched, with awards given to states and the Federal District for achieving goals in the implementation and operation of the RNQA. The program is divided into phases lasting 60 months, with the second phase being implemented in 2023 (ANA, 2016). Figure 03 shows the timeline of these milestones in the creation of the RNQA, which is the materialization of the Brazilian federal government's incentive for water quality monitoring. Within the study area, which is the Amazon River Basin, there were no state water quality monitoring networks until the creation of the RNQA, except for Mato Grosso, which has had water quality data records kept by its state secretariat since 2006. The only systematic water quality data before the RNQA within the study area were those from the RHN. Thus, with the creation of the RNQA, there is now another systematic and specific network for water quality. This study also presents the Hidrosat systematic network (Integrated System for the management, processing, and dissemination of hydrological data obtained from satellites), managed by ANA, which contains satellite-estimated water quality data with continuous series records since 1985. Similarly, the research extends to the systematic network created at the University of the State of Amazonas (UEA), with a structure of state-of-the-art laboratories that systematically monitor the main basins in this study area through the creation and coordination of the Program for Monitoring Water, Air, and Soil Quality in the State of Amazonas (ProQAS/AM). The following sections present and analyze each of these systematic networks. 4.1 National Hydrometeorological Network The RHN focuses on quantitative data, and monitoring guidelines have been in place since 1920 for studies on hydroelectric power use (DIAS, 2022). The focus on multiple uses of water has developed over time. The RHN data are available on the hidroweb platform and were consulted from the fluviometric stations. This research found water quality data from 30 stations dating from between 1975 and 1985. However, these series were interrupted in the late 1980s, and monitoring only resumed in the 2000s. Currently, measurement campaigns take place four times a year according to a schedule established by the SGB for each campaign. Since the use of multiparametric sondes began in the early 2010s, electrical conductivity, pH, turbidity, temperature, and dissolved oxygen have been measured. Figure 04 shows the spatial distribution of RHN monitoring points identified by the range of years in which water quality monitoring began, to illustrate the evolution of monitoring, which totals 207 active measurement points. Since 2005, there has been a visible increase in monitoring, and since 2015, it has been concentrated in the southwestern region and part of the center of the basin, as shown in Figure 04. Only sub-basin 19 (at the mouth of the Amazon River and covering part of the state of Amapá) has a monitoring point: the Pacajás station in Pará. In the westernmost part of sub-basin 16 of the Trombetas River, there is low point coverage because it is a region that is difficult to access for obtaining conventional hydrological data. The same is true in the southern part of sub-basin 14 of the Negro River. Regarding the availability of information, historical series were analyzed to identify stations with many gaps over time. Figure 05 shows the identification of stations with up to 20% missing data and more than 20% missing data in the series. Periods with many gaps make it difficult to analyze the data statistically, such as studies of trends in water quality parameters. The stations have a significant amount of series with gaps in the Rio Negro sub-basin and on the left bank of the Amazon River and in the south of sub-basins 15 (Madeira) and 17 (Tapajós). In the west of the basin, the historical series are unbroken, but they are the ones with the most recent start of observation, as seen in Figure 04. Even with these limitations, the study by Zanin et al. (2024) was able to evaluate the water quality parameters of these stations after a consistency analysis to understand how protected areas tend to improve water quality. 4.2 Surface Water Quality Monitoring Network (RNQA) The RNQA is coordinated by the states and operated by state environmental agencies. As such, management of the RNQA is decentralized, as defined by the National Water Resources Policy, which emphasizes systematic management without dissociating aspects of quantity and quality (BRAZIL, 1997). According to ANA (2013), the purpose of the RNQA is: "to analyze trends in the evolution of surface water quality, assess whether current quality meets the uses established by the classification of water bodies, identify critical areas in terms of water pollution, assess the effectiveness of management actions to restore water quality, and support planning, concession, licensing, and inspection actions." The RNQA is also linked to the RNH, so all data measured in the campaigns are made available on the hidroweb to enable systematic management. The procedures for collecting and preserving environmental samples to be used in the RNQA operation must comply with the provisions of the latest edition of the National Guide for the Collection and Preservation of Water, Sediment, Aquatic Communities, and Liquid Effluent Samples, as established by Resolution No. 207 (ANA, 2024). Annex II of Resolution No. 903 (ANA, 2013), which establishes criteria for the RNQA, presents the twenty-three minimum parameters for water quality monitoring in the RNQA for lotic and lentic environments. The campaigns are conducted twice a year to cover the rainy and less rainy seasons. For the implementation of the RNQA, the Qualiágua program (ANA, 2016) was created, establishing the minimum targets to be met by the states. All states in the study area joined the program, which has two phases of 60 months each. Only the states of Amazonas and Amapá have not yet completed phase 1, and all have joined phase 2. Although the RNQA presents the minimum parameters in Annex II of ANA (2013), in practice, each state has its own capacity to measure the parameters, as shown in Table 01. When analyzing Table 01, it can be inferred that the state of Rondônia is the only state that monitors all the minimum parameters provided for, with Mato Grosso being the second state with the highest number of parameters and measuring the parameter Dissolved Orthophosphate (mg/L of P), which is not provided for in Annex II of ANA (2013) . Acre and Roraima monitor the same parameters, while Amazonas is the state that most recently joined the program. The state of Pará does not appear in the table because it does not have any measurement points within its boundaries that make up the study area, as the RNQA in this state is concentrated in the eastern region of the state, as shown by Dias et al. (2025). The state with the highest number of points is Amazonas, with 54 measurement points, and the state with the lowest number is Acre (only 5 points). The allocation of points is determined in conjunction with state secretariats, which define strategic points, impact points, and reference points with the aim of eliminating geographical and temporal gaps in monitoring (ANA, 2013). The total number of RNQA monitoring points in the Amazon basin is 152. Table 1 - Parameters measured at RNQA monitoring points in the Amazon River basin State Qualiágua Affiliation Number of points in 2024 Parameters Acre 2016 5 CE, T, Turb, OD, pH, DBO, STD, E. coli , CT Amapá 2018 9 CE, T, OD, pH e Turb Amazonas 2020 54 CE, T, Turb, OD, pH, Cl - ,Alcal, STD, SST, DBO, DQO, CT, FT, NT Mato Grosso 2017 32 CE, T, Turb, OD, pH, STD, SST, Alcal, Cl - Total, DBO, DQO, E. coli , Orto D, FT, Nit, NA, NT Rondônia 2016 27 CE, T, Turb, OD, pH, STD, SST, Alcal, Cl - Total, Transp, DBO, DQO, COT, CT, Clorof, Fito, FSR, FT, Nit, NA, NT Roraima 2016 25 CE, T, Turb, OD, pH, DBO, STD, E. coli , CT Legenda: CE: Electrical Conductivity (µS/cm); T: Water and Air Temperature (°C); Turb: Turbidity (UNT); OD: Dissolved Oxygenmg/L de O 2 ); pH; STD: Total Dissolved Solids (mg/L); SST: Total Suspended Solids (mg/L); DBO: Biochemical Oxygen Demand (5d, 20°C, mg/L de O 2 ); DQO: Chemical Oxygen Demand (mg/L de O 2 ); Cl - : Chlorides (mg/L); Cl - Total: Total Chloride (mg/L de Cl); Alcal: Total Alkalinity (mg/L de CaCO 3 ); Orto D: Dissolved Orthophosphate (mg/L de P); COT: Total Organic Carbon (mg/L como C); FT: Total Phosphorus (mg/L de P); FSR: Reactive Soluble Phosphorus (mg/L de P); Nit: Nitrate (mg/L de N); NA: Ammoniacal Nitrogen (mg/L de N); NT: Total Nitrogen (mg/L de N); Transp: Water Transparency (m); Clorof: Chlorophyll α (µg/L); Fito: Phytoplankton - quantitative and qualitative (number of cells/mL); E. coli : Escherichia coli (UFC/100mL); CT: Total Coliforms (NMP/100mL). As for the year in which monitoring began and its spatial distribution, Figure 06 shows that the points are well distributed in the state of Rondônia, which has the most complete network compared to the other states, followed by the state of Roraima, even with gaps in the northwest of the state, which is possibly related to logistical difficulties in operating in isolated areas. In the state of Acre, the points are concentrated in areas bordering Peru and Bolivia, and in Amapá they are grouped at the mouth of the Amazon River. In the state of Amazonas, the points are distributed at the exutory of sub-basin 14 of the Rio Negro and in sub-basin 16 of the Amazon River between the Madeira and Trombetas rivers. In Mato Grosso, the monitoring points are concentrated in the south-central part of sub-basin 17 of the Tapajós River, with three points in the south of sub-basin 18 of the Xingu River. Analyzing the Amazon River basin as a whole, the RNQA points are very concentrated in certain areas and need to be expanded in order to achieve the established goal of covering geographical gaps. In the analysis of historical series, the data is very recent (from 2015 onwards), with the exception of Mato Grosso, which has data measured since 2006, and has few gaps (less than 20%), with most points having a complete series. The RNQA is an evolution, as it provides incentives such as Qualiágua for states that are managing to carry out the operation within their particularities. 4.3 Hidrosat Virtual Network Hidrosat was created to give visibility and systematize the Technical Cooperation Project for Hydrological Spatial Monitoring of Large Basins developed by ANA and the French institute IRD (Institut de Recherche pour le Développement). The virtual stations obtained through the use of space sensors embedded in satellites can estimate sediment concentrations, turbidity, and chlorophyll-α, as well as the elevations of virtual stations distributed throughout South America. Water quality data are obtained from the processing of images from MODIS (MODerate resolution Imaging Spectroradiometer) sensors onboard NASA's Terra and Aqua satellites for over 20 years, sensors from the Landsat family (TM, ETM+, and OLI), and MSI/Sentinel-2 (Carvalho et al., 2015). The platform is used for various studies, such as: the assessment of sediment transport carried out by Benatti et al. (2024), in comparative studies between real stations and hydro-sedimentological models that show that they have the same order of magnitude, as done by Silva et al. (2024) in five important Brazilian rivers and in the comparison of sediment key curves with measured data and by Hidrosat, which expands the collection of information where there are no conventional measurements (Condé et al., 2020). Regarding water quality information in the study area of this work, only the parameter Suspended Sediment (mg/L) has been measured, distributed along the Amazon River with data from 2000 onwards (with one monitoring point in Peru) and distributed across nine virtual stations and along the Madeira River, spatialized across 12 virtual monitoring points since 1985 with Landsat data. The spatial distribution of these points and the year monitoring began can be analyzed in Figure 07. 4.4 Amazonas State Water, Air, and Soil Quality Monitoring Program (ProQAS/AM) According to Guestrim et al. (2022), ProQAS/AM, created in 2022 and conceived by researchers at UEA, is one of the largest environmental monitoring programs in the world currently in operation, aimed at preserving the Amazon. It has 12 environmental monitoring projects and maintains partnerships with Harvard University, the University of Geneva, and the Max Planck Institute. ProQAS/AM develops water quality monitoring actions that aim to understand and monitor the conditions of water, soil, and air and scenarios in the face of extreme events (Mamede et al., 2023). The main product of the program is the development of a standardized WQI for blackwater rivers in the Amazon, in which an index was constructed with a total of 342,930 analyses involving 161 parameters between 2021 and 2023, which reflects the reality of the Amazon River and is no longer based on the classic WQI developed in other Brazilian regions (Duvoisin et al., 2025). The projects currently being implemented by ProQAS/AM are: Water Quality Monitoring in Greater Manaus, the Madeira River (within the boundaries of the State of Amazonas), the Negro River (between Manaus and São Gabriel da Cachoeira), and the Solimões River (Stage 1: Tefé to Manaus). For the other sections, funding is needed to expand monitoring, such as the Purus, Juruá, Japurá, Solimões (between Tefé and Tabatinga), Amazonas (between Manaus and Parintins), and Frontier and Transboundary Rivers. To be considered systematic monitoring, parameters must be standardized and measured data must be available. When consulting the program's website https://www.gp-qat.com/, the freely available data are those from the Greater Manaus Water Quality Monitoring, which is carried out in four basins within the limits of the capital of Amazonas, as shown in Figure 08, and which totals 55 monitoring points. The other projects mentioned do not yet have water quality data available on their own websites and, therefore, were not spatialized in this study. The data has been available since 2022 and has already been classified by the IQA developed by Duvoisin et al. (2025) for blackwater rivers and presents the results of the following parameters: Ammoniacal Nitrogen, Total Phosphorus (mg/L), pH, Turbidity (NTU), Dissolved Solids, Thermotolerant Coliforms (NMP/100mL), Conductivity, Biochemical Oxygen Demand (mg/L), and Dissolved Oxygen (%). In addition, the campaigns are conducted four times a year and the entire database is consistent, with no missing data for the period from 2022 to 2024. 4.5 Overview of Systematic Water Quality Monitoring Knowledge of water quality data is extremely important for water management, as the study shows that the four networks presented have characteristics. Figure 09 shows the evolution of the start year of each monitoring point of the four networks over time. By analyzing them together (Figure 09), it is possible to confirm the importance of RHN as a pioneer in systematically obtaining water quality data in the Amazon River basin, with the first stations in the 1970s, and as the network with the largest number of measurement points and best distribution throughout the study area. The period from 1986 to 1995 is marked by the absence of any new monitoring stations, and when analyzing the historical series of the RHN, there are periods with missing data at many stations (analyzed in Figure 05). Until 2005, there were also few new stations, but the scenario changed in 2015, when the RNQA began operating and maintained a growing number of new points in 2016, 2017, 2018, 2019, and 2023. The year 2022 is marked by the start of systematic monitoring by ProQAS/AM, with 55 points added this year. This shows that there is a history of monitoring, but that it has been intensified since the creation of the RNQA. The total number of points up to the year 2024 is 435 (RHN: 207 points, RNQA: 152 points, Hidrosat: 21 points, and ProQAS/AM: 55 points), all of which are shown in Figure 10. As for the density of the points, the analysis is carried out in accordance with the recommendation of Resolution No. 903 (ANA, 2013), which, for the states of Region 1, is 1 point per 10,000 km², which in this case considers the area of each sub-basin. Each density per sub-basin is shown in Figure 10. Sub-basins 14 Rio Negro and 15 Rio Tapajós are the only ones that meet the recommendation. However, when analyzing spatial distribution, in the Rio Negro sub-basin the points are concentrated in the southern region and at the outlet, while in the center there is no coverage. This concentration in certain areas also occurs in other sub-basins, such as at the headwaters of sub-basin 16. The Xingu and Paru sub-basin 18 have the lowest density (0.35). Thus, the assessment should not be based solely on density, as this can “mask” the information, as occurs in sub-basin 14, which meets the recommendation, but where the points are concentrated in certain areas. In addition, the design of the water quality network has several methodologies that began in the 1960s (SANDERS et al., 1983; SHARP, 1971) and, according to Cruz (2024), there is still no universally accepted methodology for network design, and it is common to find networks designed arbitrarily and without methodological criteria. Therefore, more robust research is needed to assess whether the current configuration in Figure 10 is the most efficient, as monitoring should be sought that is not costly, such as redundant monitoring points or those with excessive parameters. Systematic information exists within the study area and can be used for numerous studies and to establish standards. However, the data are limited to a few parameters in the oldest network with the best spatial coverage, which is the case of the RHN, or have many analysis parameters but do not cover the entire study area (RNQA and ProQAS/AM). 5. CONCLUSION The study allowed for the analysis of existing information on surface water quality in one of the most important rivers on the planet. Knowing the quality of water is essential to comply with environmental guidelines and ensure the safety of water resource users. Based on this research, it is possible to conduct various studies using the databases presented here, such as the evaluation of the design of systematic networks, statistical analysis of water quality data, rivers with potentially degraded water quality in the Amazon Basin, and network optimization methods. Continuous, standardized monitoring with data availability is essential to understanding the dynamics of water bodies and their particularities. The research showed that the creation of a specific network for water quality is recent, with the establishment of the RNQA, and reinforces the incentive for initiatives such as ProQAS/AM, which develop cutting-edge monitoring with international partnerships. The data generated by the RHN is the most widely distributed and most frequently, even though it is limited in terms of the number of parameters. Integrating all this information is essential for monitoring the rivers of the Amazon basin, which in recent years have been impacted by extreme weather and anthropogenic activities that affect the quality of water bodies. Declarations Funding To the Coordination for the Improvement of Higher Education Personnel (CAPES) for funding the research with scholarships. Author Contribution Dias LC contributed to the conception of the article and main text, Fernandes LM and Teixeira LCGM supervised the entire research and reviewed it, Ferreira HS designed the maps, Lima JBM contributed to data collection, and Oliveira VS reviewed the article. Data Availability All data in the article are available free of charge on the hidroweb portal: https://www.snirh.gov.br/hidroweb/apresentacao and on the website: https://www.gp-qat.com/proqas References Aboutalebi M, Bozorg-Haddad O, Loáciga, HA (2016) Multiobjective design of water-quality monitoring networks in river-reservoir systems. Journal of Environmental Engineering, v. 143, n. 1, 2016. https://doi.org/10.1061/(ASCE)EE.1943-7870.00011. National Water Agency – ANA (1972) DNAEE River Basins: geospatial metadata. Updated 2021. https://metadados.snirh.gov.br/geonetwork/srv/api/records/43539328-3a83-4bf2-9cea-2b47513f4b07 National Water Agency – ANA (2012) Overview of surface water quality in Brazil: 2012. ANA, Brasília. National Water Agency – ANA (2013) Resolution No. 903, of August 7, 2013. Guidelines for the National Water Quality Monitoring Network (RNQA). National Water Agency – ANA (2016) Resolution No. 643, dated June 27, 2016. QUALIÁGUA Program. National Water Agency – ANA (2022) Regulatory Outcome Assessment Report: Qualiágua. ANA, Brasília. National Water Agency – ANA (2023) Technical Note No. 62/2023/SGH. National Hydrometeorological Network. National Water Agency – ANA (2024) Resolution No. 207, dated September 2, 2024. Procedures for collecting and preserving environmental samples for the RNQA. Benatti R et al. (2024) Assessment of sediment transport in the Doce River using remote sensing data. Proceedings of the II National Symposium on Fluid Mechanics and Hydraulics. Brazil (1997) Law No. 9,433, of January 8, 1997. National Water Resources Policy. Official Gazette of the Union. Bringel SRB, Gutierrez DMD (eds) (2024) Waters of the Amazon: nature and contemporary challenges. INPA Publishing House, Manaus. https://doi.org/10.61818/56330525 Camara M et al. (2020) Economic and efficiency-based optimization of water quality monitoring network for land use impact assessment. Science of the Total Environment 737:139800. https://doi.org/10.1016/j.scitotenv.2020.139800 Carvalho JC et al. (2015) HIDROSAT – Integrated system for satellite-based hydrological data management. Proceedings of the XXI Brazilian Symposium on Water Resources. Chang CL, Lin YT (2014) Using the VIKOR method to evaluate the design of a water quality monitoring network. International Journal of Environmental Science and Technology 11:303–310. https://doi.org/10.1007/s13762-013-0195-2 Chapman D, Kimstach V (1992) Selection of water quality variables. In: Chapman D (ed) Water quality assessments. Chapman and Hall, London, p 51–119. https://doi.org/10.4324/9780203476710 Coutinho EC et al. (2019) Variability of the hydrological regime of the Amazon Basin. Boletim de Geografia 37(2):129–147. Condé RCC et al. (2020) Key sediment curves and remote sensing in the São Francisco River. Proceedings of the XIV National Meeting on Sediment Engineering. Cruz FM (2024) Emergency water quality monitoring programs. Doctoral thesis, Federal University of Minas Gerais. Dias LC (2022) Analysis of precipitation and flow trends in the Amazon River basin. Master's thesis, Federal University of Pará. Dias LC et al. (2025) Diagnosis of the water quality monitoring network in Pará. Proceedings of the 33rd Brazilian Congress of Sanitary and Environmental Engineering. Destandau F, Zaiter Y (2020) Spatio-temporal design for a water quality monitoring network. Water Resources and Economics 32:100156. Duvoisin S Jr et al. (2025) A water quality index for blackwater rivers of the Amazon region. 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IBGE, Rio de Janeiro. https://cidades.ibge.gov.br/ Ishihara JH et al. (2014) Quantitative and spatial assessment of precipitation in the Brazilian Amazon (Legal Amazon) (1978–2007). Revista Brasileira de Recursos Hídricos 19(1):29–39. Jiang J et al. (2020) Review on design and optimization of surface water quality monitoring networks. Environmental Modelling & Software 132:104792. https://doi.org/10.1016/j.envsoft.2020.104792 Karaman HG (2013) Identifying uncertainty of the mean of some water quality variables along the water quality monitoring network of the Bahr El Baqar drain. Water Science 27:48–56. https://doi.org/10.1016/j.wsj.2013.12.005 Kyna Borel DP, Vance C, Karthikeyan R (2017) Optimization of a water quality monitoring network using a spatially referenced water quality model and a genetic algorithm. Water 9:704. https://doi.org/10.3390/w9090704 Libos NMC, Pinheiro A, Girardi R (2023) Spatial analysis of water quality monitoring data in Santa Catarina. Brazilian Journal of Physical Geography 16(2):672–687. Lima JBM, Dias LC (2015) Ten-year evolution of the flows of the Amazon River and its tributaries. Proceedings of the XXI Brazilian Symposium on Water Resources . Brasília. Lira BRP (2019) Assessment of rainfall behavior and trends in the Legal Amazon from 1986 to 2015. Master’s dissertation, Federal University of Pará, Belém. Liyanage CP, Marasinghe A, Yamada K (2016) Comparison of optimized selection methods of sampling site networks for water quality monitoring in a river. International Journal of Affective Engineering 15(2):195–201. https://doi.org/10.1007/s13762-013-0195-2 Mamede JEL et al. (2025) The drought in the Amazon in 2023: reflections on the impacts on socioeconomic biodiversity. Unifunec Científica Multidisciplinar 14(16):1–13. https://doi.org/10.24980/ucm.v14i16.6329 Medeiros AC (2012) Obtaining the IQA for assessing water quality in rivers in the municipalities of Abaetetuba and Barcarena (PA). Master’s dissertation, Federal University of Pará, Belém. Molinier M et al. (1995) Hydrology of the Amazon River Basin. Science and Technology :32–36. Amazon Cooperation Treaty Organization – ACTO (2023) Executive summary of the report on the status of water quality in the Amazon Basin. ACTO, Brasília. Ringel SRB, Gutierrez DMD (eds) (2024) Waters of the Amazon: nature and contemporary challenges. Brazilian Water Museum; INPA, Manaus. Rudorff NM et al. (2011) Classification of water types in optically complex waters. INPE, São José dos Campos. Sanders TG et al. (1983) Design of networks for monitoring water quality . Water Resources Publications, Colorado. Sharp WE (1971) A topologically optimum water-sampling plan for rivers and streams. Water Resources Research 7(6):1642–1646. Sioli H (1984) The Amazon: limnology and landscape ecology of a mighty tropical river. Junk Publishers, Dordrecht. Telci IT, Man K, Guan J, Aral MM (2009) Optimal water quality monitoring network design for river systems. Journal of Environmental Management 90(10):2987–2998. https://doi.org/10.1016/j.jenvman.2009.04.011 Zanin PR et al. (2024) Do protected areas enhance surface water quality across the Brazilian Amazon? Journal for Nature Conservation 81:126684. https://doi.org/10.1016/j.jnc.2024.126684 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Jan, 2026 Editor assigned by journal 15 Jan, 2026 Submission checks completed at journal 15 Jan, 2026 First submitted to journal 14 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8602785","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575116258,"identity":"87af2fdd-2d24-4ee1-84d0-58d2b38040c9","order_by":0,"name":"Luanna Costa Dias","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACPijN2AbEDxgM4BIHcGphQ9LCbECalgYgWwJJAo8WieRnjwtqGGT72I8/q/xRcEdet4H94mMehjv5uLWkmRvPOMZg3MaTY3abx+CZ4bYDPMXGPAzPLBtwakkwk+ZhY0hsY8hhu81gcJgRqCVNcgbDYQMcOoBa0r9J8/wDauF//qzwh8FheyK05JhJ87YBtQCtY+AxOJy47QD7MYkP+LTwvCmT5u2TMG6TeGMsDfRL8rbDPMwGHwye4dTCz56+TZrnm43s/P70hx9//Llju+14+8MHCRV3cGqBAniMHADGKA9QNSENSACohYH9AfHqR8EoGAWjYCQAAPUSUpSmwzS7AAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of Para","correspondingAuthor":true,"prefix":"","firstName":"Luanna","middleName":"Costa","lastName":"Dias","suffix":""},{"id":575116259,"identity":"d9f47d60-f38f-469f-8e6d-f890fe3816b2","order_by":1,"name":"Luiza Carla Girard Mendes Teixeira","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Luiza","middleName":"Carla Girard Mendes","lastName":"Teixeira","suffix":""},{"id":575116260,"identity":"daa442d2-c8b3-4faa-84df-273f96e733a1","order_by":2,"name":"Lindemberg Lima Fernandes","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Lindemberg","middleName":"Lima","lastName":"Fernandes","suffix":""},{"id":575116261,"identity":"c2a39594-e46e-4ac2-ac81-4fb190a49e65","order_by":3,"name":"João Batista Marcelo de Lima","email":"","orcid":"","institution":"Geological Survey of Brazil","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"Batista Marcelo","lastName":"de Lima","suffix":""},{"id":575116262,"identity":"d74f09ea-2608-483a-aa06-06e2c9cfd8bf","order_by":4,"name":"Hugo de Souza Ferreira","email":"","orcid":"","institution":"Geological Survey of Brazil","correspondingAuthor":false,"prefix":"","firstName":"Hugo","middleName":"de Souza","lastName":"Ferreira","suffix":""},{"id":575116263,"identity":"b81f3f77-d9ac-4354-b70c-cc4c7d9f3358","order_by":5,"name":"Vinicius Silva de Oliveira","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Vinicius","middleName":"Silva","lastName":"de Oliveira","suffix":""}],"badges":[],"createdAt":"2026-01-14 14:24:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8602785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8602785/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104828818,"identity":"9ec67150-b8a4-4081-8d09-903a6f15790a","added_by":"auto","created_at":"2026-03-17 16:06:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236651,"visible":true,"origin":"","legend":"\u003cp\u003eDNAEE Hydrographic Division – Amazon River Basin. Source: Dias et al. (2022)\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/e3d4ad5c108251748604ef16.jpg"},{"id":104835327,"identity":"f509110f-0b75-4a73-8080-33d2b3e500e8","added_by":"auto","created_at":"2026-03-17 17:43:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75377,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for identifying systematic networks\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/12b5ade3b8daee524f9af2ca.jpg"},{"id":104828819,"identity":"8f6cb9b8-ac09-4a47-9051-0fb52dd9582b","added_by":"auto","created_at":"2026-03-17 16:06:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33322,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal evolution of systematic water quality monitoring at the federal level of the Brazilian government\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/25333bc36990ed9c196b60fb.jpg"},{"id":105033476,"identity":"94523ded-438c-4713-94f1-418766556b16","added_by":"auto","created_at":"2026-03-20 07:17:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":253450,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of RHN points with progress since the start of operations.\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/5305d1906f5fd5766d5427f6.jpg"},{"id":104828825,"identity":"a30b6817-9500-47f6-8a0d-4349c2cc31e0","added_by":"auto","created_at":"2026-03-17 16:06:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":255463,"visible":true,"origin":"","legend":"\u003cp\u003eAvailability of RHN data\u003c/p\u003e","description":"","filename":"image5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/329af6513b5ad38f5d7f784d.jpg"},{"id":104828824,"identity":"ac355971-c835-4589-b74d-e94b3b2cbe51","added_by":"auto","created_at":"2026-03-17 16:06:19","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":205906,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of RNQA points with progress since the start of operations\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/db2373057a8cae120cd49d06.jpeg"},{"id":104835611,"identity":"cf00bb35-1507-46c3-bf13-42c35550ee46","added_by":"auto","created_at":"2026-03-17 17:46:47","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":203742,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of virtual stations measuring suspended sediment (mg/L) from Hidrosat (ANA) and year monitoring began.\u003c/p\u003e","description":"","filename":"image7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/31f0bca76c576608968af0bc.jpg"},{"id":104828822,"identity":"431ee140-cd27-48ff-8646-4225ed74154e","added_by":"auto","created_at":"2026-03-17 16:06:19","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":207820,"visible":true,"origin":"","legend":"\u003cp\u003eProQAS/AM systematic monitoring points\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/7ff5eba89c1f300bbcdbdaa9.jpeg"},{"id":104828826,"identity":"5c27b7eb-289f-4b1d-a797-7141cffd410b","added_by":"auto","created_at":"2026-03-17 16:06:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":40046,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of monitoring points initiated over time by the four systematic monitoring networks in the Amazon River basin\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/9dd9cda45fbed8fb50932982.png"},{"id":104828827,"identity":"6eb069ef-bf57-4df0-93f9-83691a6f6512","added_by":"auto","created_at":"2026-03-17 16:06:19","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":292797,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and density of RHN, RNQA, Hidrosat, and ProQAS/AM points\u003c/p\u003e","description":"","filename":"image10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/53155de7225961e0e53513d5.jpg"},{"id":105036452,"identity":"638dbde0-8a8f-4ad7-92b6-19b115af315b","added_by":"auto","created_at":"2026-03-20 07:33:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2397493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8602785/v1/7de1052e-4871-41d0-81d7-d3d100128cc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Overview of systematic monitoring networks for surface water quality in the Amazon River Basin","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSurface water is the most important and accessible source of water for human life and agro-industrial production. However, because it is easy to collect, it is the most polluted in many countries (Aboutalebi et al. 2016; Jiang et al. 2020).\u003c/p\u003e\n\u003cp\u003eGiven this, monitoring water quality is essential for water resource management, as it is important for understanding and protecting aquatic environments, elucidating the processes that affect water quality, and detecting and analyzing spatial and temporal trends.\u003c/p\u003e\n\u003cp\u003eThe topic of design and optimization of water quality monitoring networks is frequently studied around the world. The first monitoring networks appeared in the 1960s (Sanders et al. 1983), but according to Chapman and Kimstach (1992), modern monitoring began in the 1950s with a focus solely on data collection. Sharp (1971) conducted one of the first studies in the literature to optimize water quality monitoring points, with the objective of locating sources of pollution.\u003c/p\u003e\n\u003cp\u003eFor classic authors on this topic (Sharp 1971; Sanders et al., 1983; Chapman and Kimstach 1992; Harmancioglu et al. 1999), the definition of water quality monitoring is to obtain physical, chemical, and biological control of water characteristics using statistical sampling. These works highlight the complexity of monitoring, the need to optimize the network, and emphasize the particularities and difficulties for developing countries, as mentioned by Gradilla-Hern\u0026aacute;ndez et al. (2022): \u0026ldquo;in countries in the southern hemisphere, water quality monitoring networks are inefficient due to the use of subjective strategies and insufficient investment\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eEven with the limitations of these studies, as they date from the late 20th century, there was already concern about disseminating procedures and methodologies for optimizing the water quality monitoring network. Since the 2000s, there have been a large number of articles on a wide variety of network optimization methods, such as: the use of entropy (Karaman 2013), the application of hydrodynamics to contaminant transport (Telci et al. 2009), \u0026nbsp;use of multivariate and cluster analysis (Guigues et al. 2013), \u0026ldquo;VIKOR\u0026rdquo; multi-criteria decision making (Chang and Lin 2013), genetic algorithms for network optimization (Liyanage et al. 2016; Kyna Borel et al. 2017;), Bayesian method (Destandau and Zaiter 2020), and geostatistical techniques of Kendall\u0026apos;s W (Camara et al. 2020).\u003c/p\u003e\n\u003cp\u003eAccording to Sharp (1971), the three stages of network design are: selection of indicator parameters, definition of sampling locations, and determination of collection frequency. In addition, the specific objectives of monitoring, easy access to sampling points, representativeness in the watershed, surveillance of pollution sources, estimation of pollutant loads, and water use must be considered (Aboutalebi et al. 2016).\u003c/p\u003e\n\u003cp\u003eBased on Destandau and Zaiter (2020), there are two types of networks: surveillance control networks and operational control networks (which are temporary). The surveillance control network is like the monitoring networks operated by government agencies to comply with their environmental legislation objectives. This type of network is also known as systematic monitoring, which, according to Libos et al. (2022), consists of fixed sampling points with defined parameters, methodologies, and frequencies. The operational control network, on the other hand, corresponds to the self-monitoring of potentially polluting companies, which is also called non-systematic monitoring, as it is not standardized and has a random frequency.\u003c/p\u003e\n\u003cp\u003eIn Brazil, water monitoring networks were established based on state monitoring, without any standardization, with the first Brazilian networks beginning in the 1970s (ANA 2012). In 1974, CETESB (S\u0026atilde;o Paulo State Environmental Company) began monitoring the water quality of rivers and reservoirs to control pollution, and adapted the Water Quality Index (WQI), which is widely used in Brazil (Medeiros 2012).\u003c/p\u003e\n\u003cp\u003eUntil 2014, Brazil\u0026apos;s surface water quality monitoring networks were not standardized, with only 17 Brazilian states monitoring water quality systematically. It was only with the creation of the Surface Water Quality Monitoring Network (RNQA) by the National Water and Basic Sanitation Agency (ANA) in 2013 that guidelines for operation and systematic monitoring were established (ANA 2012).\u003c/p\u003e\n\u003cp\u003eIn 2016, all states in the Northern Region of Brazil, with the exception of Tocantins, did not have a state monitoring network (ANA 2022), which means that the rivers in these states, most of which are part of the Amazon River basin, lack adequate knowledge of water quality in the world\u0026apos;s largest hydrographic basin.\u003c/p\u003e\n\u003cp\u003eGiven this scenario, the objective of this study is to assess the current situation of surface water quality monitoring networks in the Amazon River basin through a spatiotemporal analysis of existing points, measured parameters, and data frequency. Thus, an overview of surface water quality monitoring in an area of global relevance is presented considering the current climate change scenario, which tends to affect water quality for its various uses.\u003c/p\u003e"},{"header":"2. STUDY AREA","content":"\u003cp\u003eThe division of Brazilian territory into river basins was carried out in 1972 by the old National Department of Water and Electric Power (DNAEE), with the objective of implementing an information system and codifying the fluviometric stations that make up the National Hydrometeorological Network (RHN) (ANA 2021). In this division, there are nine hydrographic basins, each of which is subdivided into ten sub-basins, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003e(a). Basin 1 corresponds to the Amazon River, which is the study area, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003e (b).\u003c/p\u003e \u003cp\u003eThe Amazon River basin covers approximately 6.3\u0026nbsp;million km\u0026sup2; in Brazil and includes the states of Acre, Amazonas, Rond\u0026ocirc;nia, and Roraima, as well as parts of the states of Amap\u0026aacute;, Mato Grosso, and Par\u0026aacute; (Dias et al. 2022). It has a population of approximately 12\u0026nbsp;million inhabitants, which corresponds to about 5% of Brazil's population, with the most populous urban centers being Manaus (2\u0026nbsp;million inhabitants), Porto Velho (517,000 inhabitants), and Rio Branco (387,000 inhabitants) (IBGE 2023).\u003c/p\u003e \u003cp\u003eAccording to ANA (2012) and OTCA (2023), the main pressures on water quality in the Amazon basin are: domestic sewage and solid waste, industrial activities such as the Manaus Free Trade Zone, mining and prospecting in the Tapaj\u0026oacute;s and Madeira basins, deforestation and inadequate soil management (arc of deforestation in the south of the basin), hydroelectric exploitation, navigation (which is the only means of transport in many places), and the effects of droughts such as those of 2005, 2010, 2023, and 2024.\u003c/p\u003e \u003cp\u003eThe natural conditions of the waters of the Amazon River basin are determined by the geology and vegetation that establish physical and chemical characteristics (ANA 2012). In the Amazon basin, there are important areas of sedimentation originating in the Andes (Brigel and Gutierrez, 2024). The colors they take on were classified by Sioli (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) using geological formation, water color, sediment load, electrical conductivity, and humus formation (organic matter), which is directly related to a decrease in pH. Based on this, they were classified as white waters (neutral pH between 6.2\u0026ndash;7.2), black waters (low pH of 3.8\u0026ndash;4.9), and clear waters (pH between the extremes of white and black waters of 4.5\u0026ndash;7.8) (Rudorff et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhite waters are highly turbid, muddy in color, originate in the Andes, and are rich in mineral salts and suspended matter, such as those of the Solim\u0026otilde;es, Madeira, Juru\u0026aacute;, and Purus rivers. Black waters, on the other hand, are dark because they drain areas of plains and forests with soils rich in organic matter, such as the Negro River. Clear waters are greenish or transparent because they drain waters from crystalline areas such as ancient rocks, such as those of the Tapaj\u0026oacute;s River (Sioli \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Brigel and Gutierrez 2024; Duvoisin Jr. et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main rivers in the Amazon River basin on the right margin are the Javari, Juta\u0026iacute;, Juru\u0026aacute;, Purus, Madeira, Tapaj\u0026oacute;s, and Xingu rivers. Those on the left margin are the Japur\u0026aacute;, Negro, Trombetas, Jari, and Nhamund\u0026aacute; rivers. The mean streamflow of the Amazon River is 132,145 m\u0026sup3;/s and has a regular hydrological regime, with well-defined dry and wet seasons (ANA 2012; Coutinho et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The rainfall regime varies across its different regions, with average precipitation ranging from 2,000 to 2,500 mm/year (Lira \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fisch et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ishihara et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Molinier et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Souza et al. 2016).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. MATERIALS AND METHODS","content":"\u003cp\u003eTo achieve the objectives of the study, we first surveyed the information available at ANA, state secretariats, and public institutions (such as universities and environmental entities) on networks that systematically monitor water quality. The criteria adopted for data to be considered systematic is based on the concept presented by Destandau and Zaiter (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Libos et al. (2022), in which they consider monitoring points to be those that are spatialized, standardized, continuous over time, and with information available to the public through GIS (Geographic Information System). Given these requirements, the monitoring network is considered systematic (also classified as a surveillance control network by Destandau and Zaiter \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e02\u003c/span\u003e illustrates the flow of this stage.\u003c/p\u003e \u003cp\u003eAfter detecting the systematic networks, the information was recorded in spreadsheets and the available historical series were downloaded up to the year 2024 within the limits of the Amazon River basin according to the DNAEE classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003eb). In addition, the data series for each station was analyzed in order to identify which parameters are monitored, the operators, the frequency, failures, and gaps in the historical series. This is important to understand whether systematic monitoring has consistent information and is in fact effective.\u003c/p\u003e \u003cp\u003eThe stations of each systematic network were spatialized using QGis version 3.28 in order to analyze the geographical distribution within the study area and its respective coverage. According to Resolution No. 903 (ANA, 2013), which created the RNQA, three regions were defined for implementation, with this study focusing on Region 1, which must have at least one monitoring point per 10,000 km\u0026sup2;. Based on this, the density of points in relation to the area of each of the 10 sub-basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003eb) was determined to verify compliance with the proposed minimum density of points.\u003c/p\u003e\u003cp\u003eThe methodological steps are intended to assess whether the network is indeed systematic (Figure 02), the quality of the available data, and its spatial and temporal distribution. This provides an overview of what surface water quality data is available and where to find it.\u003c/p\u003e"},{"header":"4. RESULTS AND DISCUSSION","content":"\u003cp\u003eSystematic monitoring of surface water quality in Brazil was conceived through state initiatives in which each state adopted its own criteria for monitoring. In the 1990s, with the advent of the National Water Resources Policy (BRAZIL, 1997), which instituted decentralized and participatory management and has as one of its objectives \u0026ldquo;to ensure the availability of water at adequate quality standards,\u0026rdquo; the focus began to shift toward the creation of a national water quality monitoring network.\u003c/p\u003e\n\u003cp\u003eAccording to ANA (2012), water quality monitoring in Brazil began in the 1970s, through the measurement of some basic parameters at RHN fluviometric points and by sanitation companies with a focus on potability, such as CETESB. According to Technical Note No. 62/2023/SGH from ANA (2023), the first water quality monitoring network began in 1990 with its implementation in the Rio Doce basin. From then on, a specific network was designed to monitor water quality, rather than relying on the RHN\u0026apos;s fluviometric stations.\u003c/p\u003e\n\u003cp\u003eThe only initiative to improve these networks took place in 2008 with the launch of the National Water Quality Assessment Program (PNQA), whose main objective was to provide society with adequate knowledge about the quality of Brazilian surface waters. \u0026nbsp; One of the components of the PNQA is the Surface Water Quality Monitoring Network (RNQA), which aims to expand and optimize the geographic distribution of water quality data (ANA, 2023). The RNQA is published in ANA Resolution No. 903 (ANA, 2013), and states participate on a voluntary basis.\u003c/p\u003e\n\u003cp\u003eTo encourage states and strengthen the RNQA, in 2014, through Resolution No. 1,040, which was replaced by Resolution No. 643 (ANA, 2016), the Program to Encourage the Disclosure of Water Quality Data (Quali\u0026aacute;gua) was launched, with awards given to states and the Federal District for achieving goals in the implementation and operation of the RNQA. The program is divided into phases lasting 60 months, with the second phase being implemented in 2023 (ANA, 2016). Figure 03 shows the timeline of these milestones in the creation of the RNQA, which is the materialization of the Brazilian federal government\u0026apos;s incentive for water quality monitoring.\u003c/p\u003e\n\u003cp\u003eWithin the study area, which is the Amazon River Basin, there were no state water quality monitoring networks until the creation of the RNQA, except for Mato Grosso, which has had water quality data records kept by its state secretariat since 2006. The only systematic water quality data before the RNQA within the study area were those from the RHN. Thus, with the creation of the RNQA, there is now another systematic and specific network for water quality.\u003c/p\u003e\n\u003cp\u003eThis study also presents the Hidrosat systematic network (Integrated System for the management, processing, and dissemination of hydrological data obtained from satellites), managed by ANA, which contains satellite-estimated water quality data with continuous series records since 1985. Similarly, the research extends to the systematic network created at the University of the State of Amazonas (UEA), with a structure of state-of-the-art laboratories that systematically monitor the main basins in this study area through the creation and coordination of the Program for Monitoring Water, Air, and Soil Quality in the State of Amazonas (ProQAS/AM). The following sections present and analyze each of these systematic networks.\u003c/p\u003e\n\u003ch2\u003e4.1 National Hydrometeorological Network\u003c/h2\u003e\n\u003cp\u003eThe RHN focuses on quantitative data, and monitoring guidelines have been in place since 1920 for studies on hydroelectric power use (DIAS, 2022). The focus on multiple uses of water has developed over time.\u003c/p\u003e\n\u003cp\u003eThe RHN data are available on the hidroweb platform and were consulted from the fluviometric stations. This research found water quality data from 30 stations dating from between 1975 and 1985. However, these series were interrupted in the late 1980s, and monitoring only resumed in the 2000s. Currently, measurement campaigns take place four times a year according to a schedule established by the SGB for each campaign.\u003c/p\u003e\n\u003cp\u003eSince the use of multiparametric sondes began in the early 2010s, electrical conductivity, pH, turbidity, temperature, and dissolved oxygen have been measured. Figure 04 shows the spatial distribution of RHN monitoring points identified by the range of years in which water quality monitoring began, to illustrate the evolution of monitoring, which totals 207 active measurement points.\u003c/p\u003e\n\u003cp\u003eSince 2005, there has been a visible increase in monitoring, and since 2015, it has been concentrated in the southwestern region and part of the center of the basin, as shown in Figure 04. Only sub-basin 19 (at the mouth of the Amazon River and covering part of the state of Amap\u0026aacute;) has a monitoring point: the Pacaj\u0026aacute;s station in Par\u0026aacute;.\u003c/p\u003e\n\u003cp\u003eIn the westernmost part of sub-basin 16 of the Trombetas River, there is low point coverage because it is a region that is difficult to access for obtaining conventional hydrological data. The same is true in the southern part of sub-basin 14 of the Negro River.\u003c/p\u003e\n\u003cp\u003eRegarding the availability of information, historical series were analyzed to identify stations with many gaps over time. Figure 05 shows the identification of stations with up to 20% missing data and more than 20% missing data in the series. Periods with many gaps make it difficult to analyze the data statistically, such as studies of trends in water quality parameters.\u003c/p\u003e\n\u003cp\u003eThe stations have a significant amount of series with gaps in the Rio Negro sub-basin and on the left bank of the Amazon River and in the south of sub-basins 15 (Madeira) and 17 (Tapaj\u0026oacute;s). In the west of the basin, the historical series are unbroken, but they are the ones with the most recent start of observation, as seen in Figure 04. Even with these limitations, the study by Zanin et al. (2024) was able to evaluate the water quality parameters of these stations after a consistency analysis to understand how protected areas tend to improve water quality.\u003c/p\u003e\n\u003ch2\u003e4.2 Surface Water Quality Monitoring Network (RNQA)\u003c/h2\u003e\n\u003cp\u003eThe RNQA is coordinated by the states and operated by state environmental agencies. As such, management of the RNQA is decentralized, as defined by the National Water Resources Policy, which emphasizes systematic management without dissociating aspects of quantity and quality (BRAZIL, 1997).\u003c/p\u003e\n\u003cp\u003eAccording to ANA (2013), the purpose of the RNQA is: \u0026quot;to analyze trends in the evolution of surface water quality, assess whether current quality meets the uses established by the classification of water bodies, identify critical areas in terms of water pollution, assess the effectiveness of management actions to restore water quality, and support planning, concession, licensing, and inspection actions.\u0026quot; \u0026nbsp;The RNQA is also linked to the RNH, so all data measured in the campaigns are made available on the hidroweb to enable systematic management.\u003c/p\u003e\n\u003cp\u003eThe procedures for collecting and preserving environmental samples to be used in the RNQA operation must comply with the provisions of the latest edition of the National Guide for the Collection and Preservation of Water, Sediment, Aquatic Communities, and Liquid Effluent Samples, as established by Resolution No. 207 (ANA, 2024). Annex II of Resolution No. 903 (ANA, 2013), which establishes criteria for the RNQA, presents the twenty-three minimum parameters for water quality monitoring in the RNQA for lotic and lentic environments. The campaigns are conducted twice a year to cover the rainy and less rainy seasons.\u003c/p\u003e\n\u003cp\u003eFor the implementation of the RNQA, the Quali\u0026aacute;gua program (ANA, 2016) was created, establishing the minimum targets to be met by the states. All states in the study area joined the program, which has two phases of 60 months each. Only the states of Amazonas and Amap\u0026aacute; have not yet completed phase 1, and all have joined phase 2. Although the RNQA presents the minimum parameters in Annex II of ANA (2013), in practice, each state has its own capacity to measure the parameters, as shown in Table 01.\u003c/p\u003e\n\u003cp\u003eWhen analyzing Table 01, it can be inferred that the state of Rond\u0026ocirc;nia is the only state that monitors all the minimum parameters provided for, with Mato Grosso being the second state with the highest number of parameters and measuring the parameter Dissolved Orthophosphate (mg/L of P), which is not provided for in Annex II of ANA (2013) . Acre and Roraima monitor the same parameters, while Amazonas is the state that most recently joined the program. The state of Par\u0026aacute; does not appear in the table because it does not have any measurement points within its boundaries that make up the study area, as the RNQA in this state is concentrated in the eastern region of the state, as shown by Dias et al. (2025).\u003c/p\u003e\n\u003cp\u003eThe state with the highest number of points is Amazonas, with 54 measurement points, and the state with the lowest number is Acre (only 5 points). The allocation of points is determined in conjunction with state secretariats, which define strategic points, impact points, and reference points with the aim of eliminating geographical and temporal gaps in monitoring (ANA, 2013). The total number of RNQA monitoring points in the Amazon basin is 152.\u003c/p\u003e\n\u003cp\u003eTable 1 - Parameters measured at RNQA monitoring points in the Amazon River basin\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuali\u0026aacute;gua\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAffiliation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of points in 2024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcre\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eCE, T, Turb, OD, pH, DBO, STD, \u003cem\u003eE. coli\u003c/em\u003e, CT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmap\u0026aacute;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eCE, T, OD, pH e Turb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmazonas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eCE, T, Turb, OD, pH, Cl\u003csup\u003e-\u003c/sup\u003e,Alcal, STD, SST, DBO, DQO, CT, FT, NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMato Grosso\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eCE, T, Turb, OD, pH, STD, SST, Alcal, Cl\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTotal, DBO, DQO, \u003cem\u003eE. coli\u003c/em\u003e, Orto D, FT, Nit, NA, NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRond\u0026ocirc;nia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eCE, T, Turb, OD, pH, STD, SST, Alcal, Cl\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTotal, Transp, DBO, DQO, COT, CT, Clorof, Fito, FSR, FT, Nit, NA, NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRoraima\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eCE, T, Turb, OD, pH, DBO, STD, \u003cem\u003eE. coli\u003c/em\u003e, CT\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\u003eLegenda: CE: Electrical Conductivity (\u0026micro;S/cm); T: Water and Air Temperature (\u0026deg;C); Turb: Turbidity (UNT); OD: Dissolved Oxygenmg/L de O\u003csub\u003e2\u003c/sub\u003e); pH; STD: Total Dissolved Solids (mg/L); SST: Total Suspended Solids (mg/L); DBO: Biochemical Oxygen Demand (5d, 20\u0026deg;C, mg/L de O\u003csub\u003e2\u003c/sub\u003e); DQO: Chemical Oxygen Demand (mg/L de O\u003csub\u003e2\u003c/sub\u003e); Cl \u003csup\u003e-\u003c/sup\u003e: Chlorides (mg/L); Cl\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTotal: Total Chloride (mg/L de Cl); Alcal: Total Alkalinity (mg/L de CaCO\u003csub\u003e3\u003c/sub\u003e); Orto D: Dissolved Orthophosphate (mg/L de P); COT: Total Organic Carbon (mg/L como C); FT: Total Phosphorus (mg/L de P); FSR: Reactive Soluble Phosphorus (mg/L de P); Nit: Nitrate (mg/L de N); NA: Ammoniacal Nitrogen (mg/L de N); NT: Total Nitrogen (mg/L de N); Transp: Water Transparency (m); Clorof: Chlorophyll \u0026alpha; (\u0026micro;g/L); Fito: Phytoplankton - quantitative and qualitative (number of cells/mL); \u003cem\u003eE. coli\u003c/em\u003e: Escherichia coli (UFC/100mL); CT: Total Coliforms (NMP/100mL).\u003c/p\u003e\n\u003cp\u003eAs for the year in which monitoring began and its spatial distribution, Figure 06 shows that the points are well distributed in the state of Rond\u0026ocirc;nia, which has the most complete network compared to the other states, followed by the state of Roraima, even with gaps in the northwest of the state, which is possibly related to logistical difficulties in operating in isolated areas.\u003c/p\u003e\n\u003cp\u003eIn the state of Acre, the points are concentrated in areas bordering Peru and Bolivia, and in Amap\u0026aacute; they are grouped at the mouth of the Amazon River. In the state of Amazonas, the points are distributed at the exutory of sub-basin 14 of the Rio Negro and in sub-basin 16 of the Amazon River between the Madeira and Trombetas rivers. In Mato Grosso, the monitoring points are concentrated in the south-central part of sub-basin 17 of the Tapaj\u0026oacute;s River, with three points in the south of sub-basin 18 of the Xingu River.\u003c/p\u003e\n\u003cp\u003eAnalyzing the Amazon River basin as a whole, the RNQA points are very concentrated in certain areas and need to be expanded in order to achieve the established goal of covering geographical gaps.\u003c/p\u003e\n\u003cp\u003eIn the analysis of historical series, the data is very recent (from 2015 onwards), with the exception of Mato Grosso, which has data measured since 2006, and has few gaps (less than 20%), with most points having a complete series. The RNQA is an evolution, as it provides incentives such as Quali\u0026aacute;gua for states that are managing to carry out the operation within their particularities.\u003c/p\u003e\n\u003ch2\u003e4.3 Hidrosat Virtual Network\u003c/h2\u003e\n\u003cp\u003eHidrosat was created to give visibility and systematize the Technical Cooperation Project for Hydrological Spatial Monitoring of Large Basins developed by ANA and the French institute IRD (Institut de Recherche pour le D\u0026eacute;veloppement). The virtual stations obtained through the use of space sensors embedded in satellites can estimate sediment concentrations, turbidity, and chlorophyll-\u0026alpha;, as well as the elevations of virtual stations distributed throughout South America.\u003c/p\u003e\n\u003cp\u003eWater quality data are obtained from the processing of images from MODIS (MODerate resolution Imaging Spectroradiometer) sensors onboard NASA\u0026apos;s Terra and Aqua satellites for over 20 years, sensors from the Landsat family (TM, ETM+, and OLI), and MSI/Sentinel-2 (Carvalho et al., 2015).\u003c/p\u003e\n\u003cp\u003eThe platform is used for various studies, such as: the assessment of sediment transport carried out by Benatti et al. (2024), in comparative studies between real stations and hydro-sedimentological models that show that they have the same order of magnitude, as done by Silva et al. (2024) in five important Brazilian rivers and in the comparison of sediment key curves with measured data and by Hidrosat, which expands the collection of information where there are no conventional measurements (Cond\u0026eacute; et al., 2020).\u003c/p\u003e\n\u003cp\u003eRegarding water quality information in the study area of this work, only the parameter Suspended Sediment (mg/L) has been measured, distributed along the Amazon River with data from 2000 onwards (with one monitoring point in Peru) and distributed across nine virtual stations and along the Madeira River, spatialized across 12 virtual monitoring points since 1985 with Landsat data. The spatial distribution of these points and the year monitoring began can be analyzed in Figure 07.\u003c/p\u003e\n\u003ch2\u003e4.4 Amazonas State Water, Air, and Soil Quality Monitoring Program (ProQAS/AM)\u003c/h2\u003e\n\u003cp\u003eAccording to Guestrim et al. (2022), ProQAS/AM, created in 2022 and conceived by researchers at UEA, is one of the largest environmental monitoring programs in the world currently in operation, aimed at preserving the Amazon. It has 12 environmental monitoring projects and maintains partnerships with Harvard University, the University of Geneva, and the Max Planck Institute.\u003c/p\u003e\n\u003cp\u003eProQAS/AM develops water quality monitoring actions that aim to understand and monitor the conditions of water, soil, and air and scenarios in the face of extreme events (Mamede et al., 2023). The main product of the program is the development of a standardized WQI for blackwater rivers in the Amazon, in which an index was constructed with a total of 342,930 analyses involving 161 parameters between 2021 and 2023, which reflects the reality of the Amazon River and is no longer based on the classic WQI developed in other Brazilian regions (Duvoisin et al., 2025).\u003c/p\u003e\n\u003cp\u003eThe projects currently being implemented by ProQAS/AM are: Water Quality Monitoring in Greater Manaus, the Madeira River (within the boundaries of the State of Amazonas), the Negro River (between Manaus and S\u0026atilde;o Gabriel da Cachoeira), and the Solim\u0026otilde;es River (Stage 1: Tef\u0026eacute; to Manaus). For the other sections, funding is needed to expand monitoring, such as the Purus, Juru\u0026aacute;, Japur\u0026aacute;, Solim\u0026otilde;es (between Tef\u0026eacute; and Tabatinga), Amazonas (between Manaus and Parintins), and Frontier and Transboundary Rivers.\u003c/p\u003e\n\u003cp\u003eTo be considered systematic monitoring, parameters must be standardized and measured data must be available. When consulting the program\u0026apos;s website https://www.gp-qat.com/, the freely available data are those from the Greater Manaus Water Quality Monitoring, which is carried out in four basins within the limits of the capital of Amazonas, as shown in Figure 08, and which totals 55 monitoring points. The other projects mentioned do not yet have water quality data available on their own websites and, therefore, were not spatialized in this study.\u003c/p\u003e\n\u003cp\u003eThe data has been available since 2022 and has already been classified by the IQA developed by Duvoisin et al. (2025) for blackwater rivers and presents the results of the following parameters: Ammoniacal Nitrogen, Total Phosphorus (mg/L), pH, Turbidity (NTU), Dissolved Solids, Thermotolerant Coliforms (NMP/100mL), Conductivity, Biochemical Oxygen Demand (mg/L), and Dissolved Oxygen (%). In addition, the campaigns are conducted four times a year and the entire database is consistent, with no missing data for the period from 2022 to 2024.\u003c/p\u003e\n\u003ch2\u003e4.5 Overview of Systematic Water Quality Monitoring\u003c/h2\u003e\n\u003cp\u003eKnowledge of water quality data is extremely important for water management, as the study shows that the four networks presented have characteristics. Figure 09 shows the evolution of the start year of each monitoring point of the four networks over time. By analyzing them together (Figure 09), it is possible to confirm the importance of RHN as a pioneer in systematically obtaining water quality data in the Amazon River basin, with the first stations in the 1970s, and as the network with the largest number of measurement points and best distribution throughout the study area.\u003c/p\u003e\n\u003cp\u003eThe period from 1986 to 1995 is marked by the absence of any new monitoring stations, and when analyzing the historical series of the RHN, there are periods with missing data at many stations (analyzed in Figure 05). Until 2005, there were also few new stations, but the scenario changed in 2015, when the RNQA began operating and maintained a growing number of new points in 2016, 2017, 2018, 2019, and 2023. The year 2022 is marked by the start of systematic monitoring by ProQAS/AM, with 55 points added this year. This shows that there is a history of monitoring, but that it has been intensified since the creation of the RNQA.\u003c/p\u003e\n\u003cp\u003eThe total number of points up to the year 2024 is 435 (RHN: 207 points, RNQA: 152 points, Hidrosat: 21 points, and ProQAS/AM: 55 points), all of which are shown in Figure 10. As for the density of the points, the analysis is carried out in accordance with the recommendation of Resolution No. 903 (ANA, 2013), which, for the states of Region 1, is 1 point per 10,000 km\u0026sup2;, which in this case considers the area of each sub-basin. Each density per sub-basin is shown in Figure 10.\u003c/p\u003e\n\u003cp\u003eSub-basins 14 Rio Negro and 15 Rio Tapaj\u0026oacute;s are the only ones that meet the recommendation. However, when analyzing spatial distribution, in the Rio Negro sub-basin the points are concentrated in the southern region and at the outlet, while in the center there is no coverage. This concentration in certain areas also occurs in other sub-basins, such as at the headwaters of sub-basin 16. The Xingu and Paru sub-basin 18 have the lowest density (0.35).\u003c/p\u003e\n\u003cp\u003eThus, the assessment should not be based solely on density, as this can \u0026ldquo;mask\u0026rdquo; the information, as occurs in sub-basin 14, which meets the recommendation, but where the points are concentrated in certain areas. In addition, the design of the water quality network has several methodologies that began in the 1960s (SANDERS et al., 1983; SHARP, 1971) and, according to Cruz (2024), there is still no universally accepted methodology for network design, and it is common to find networks designed arbitrarily and without methodological criteria. Therefore, more robust research is needed to assess whether the current configuration in Figure 10 is the most efficient, as monitoring should be sought that is not costly, such as redundant monitoring points or those with excessive parameters.\u003c/p\u003e\n\u003cp\u003eSystematic information exists within the study area and can be used for numerous studies and to establish standards. However, the data are limited to a few parameters in the oldest network with the best spatial coverage, which is the case of the RHN, or have many analysis parameters but do not cover the entire study area (RNQA and ProQAS/AM).\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThe study allowed for the analysis of existing information on surface water quality in one of the most important rivers on the planet. Knowing the quality of water is essential to comply with environmental guidelines and ensure the safety of water resource users. Based on this research, it is possible to conduct various studies using the databases presented here, such as the evaluation of the design of systematic networks, statistical analysis of water quality data, rivers with potentially degraded water quality in the Amazon Basin, and network optimization methods.\u003c/p\u003e\n\u003cp\u003eContinuous, standardized monitoring with data availability is essential to understanding the dynamics of water bodies and their particularities. The research showed that the creation of a specific network for water quality is recent, with the establishment of the RNQA, and reinforces the incentive for initiatives such as ProQAS/AM, which develop cutting-edge monitoring with international partnerships. The data generated by the RHN is the most widely distributed and most frequently, even though it is limited in terms of the number of parameters. Integrating all this information is essential for monitoring the rivers of the Amazon basin, which in recent years have been impacted by extreme weather and anthropogenic activities that affect the quality of water bodies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eTo the Coordination for the Improvement of Higher Education Personnel (CAPES) for funding the research with scholarships.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDias LC contributed to the conception of the article and main text, Fernandes LM and Teixeira LCGM supervised the entire research and reviewed it, Ferreira HS designed the maps, Lima JBM contributed to data collection, and Oliveira VS reviewed the article.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data in the article are available free of charge on the hidroweb portal: https://www.snirh.gov.br/hidroweb/apresentacao and on the website: https://www.gp-qat.com/proqas\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAboutalebi M, Bozorg-Haddad O, Lo\u0026aacute;ciga, HA (2016) Multiobjective design of water-quality monitoring networks in river-reservoir systems. 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Brazilian Water Museum; INPA, Manaus.\u003c/li\u003e\n\u003cli\u003eRudorff NM et al. (2011) Classification of water types in optically complex waters. INPE, S\u0026atilde;o Jos\u0026eacute; dos Campos.\u003c/li\u003e\n\u003cli\u003eSanders TG et al. (1983) \u003cem\u003eDesign of networks for monitoring water quality\u003c/em\u003e. Water Resources Publications, Colorado.\u003c/li\u003e\n\u003cli\u003eSharp WE (1971) A topologically optimum water-sampling plan for rivers and streams. Water Resources Research 7(6):1642\u0026ndash;1646.\u003c/li\u003e\n\u003cli\u003eSioli H (1984) The Amazon: limnology and landscape ecology of a mighty tropical river. Junk Publishers, Dordrecht.\u003c/li\u003e\n\u003cli\u003eTelci IT, Man K, Guan J, Aral MM (2009) Optimal water quality monitoring network design for river systems. Journal of Environmental Management 90(10):2987\u0026ndash;2998. https://doi.org/10.1016/j.jenvman.2009.04.011\u003c/li\u003e\n\u003cli\u003eZanin PR et al. 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Journal for Nature Conservation 81:126684. https://doi.org/10.1016/j.jnc.2024.126684\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"monitoring point, historical series, systematic monitoring, Amazon, water management","lastPublishedDoi":"10.21203/rs.3.rs-8602785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8602785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater quality monitoring is essential for water resource management, especially when carried out systematically (continuously, standardized, and with data available), where it is possible to statistically analyze each parameter and generate a historical series. The Amazon River basin is important for biodiversity and climate regulation and is home to a variety of economic activities, which makes monitoring extremely valuable for decision-making. Currently, the Amazon River basin has four systematic water quality monitoring networks: the National Hydrometeorological Network (RHN), the Surface Water Quality Monitoring Network (RNQA), the Hidrosat virtual network, and the Amazonas Water, Air, and Soil Quality Monitoring Program (ProQAS/AM). The RHN is the oldest, with the best spatial distribution and the fewest parameters measured. Hidrosat only estimates the parameter of suspended sediment. ProQAS/AM is systematic only in Manaus-AM. Finally, the RNQA is a recent network established by ANA and coordinated by the states to generate continuous water quality information, but it has concentrated points that leave geographical gaps. Based on the overview presented, it is possible to conduct numerous studies with data from the systematic networks and even evaluate the efficiency of each initiative's operation, in order to obtain increasingly reliable and continuous data.\u003c/p\u003e","manuscriptTitle":"Overview of systematic monitoring networks for surface water quality in the Amazon River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 16:06:14","doi":"10.21203/rs.3.rs-8602785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-15T14:04:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-15T13:53:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-15T07:33:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Management","date":"2026-01-14T14:12:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c60bb457-3b40-424a-959b-13098c3129a0","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T15:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 16:06:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8602785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8602785","identity":"rs-8602785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}