Risk assessment of water quality using probability-consequence analysis and geospatial interpolationABSTRACT
Risk analysis is widely recognized for its simplicity and effectiveness in identifying and categorizing potential threats. It is based on two essential criteria, probability and consequence, allowing for risk prioritization and the adoption of preventive measures. In this context, the present study applies the Probability and Consequence (P&C) analysis methodology, combined with geospatial interpolation, to assess water quality and ensure water security in the municipality of Conceição do Araguaia, Pará. To achieve this, the methodological steps included parameter identification, on-site sampling, risk quantification, geostatistical modeling, and cross-validation. The results identified 19 sampling points classified between high and critical risk. The validation analysis revealed high local heterogeneity, indicating that micro-local factors drive contamination. Despite this variability, the map proved effective in identifying macroscopic zones of vulnerability, optimizing resource allocation by focusing inspections on critical clusters. The analysis showed that pH, nitrite, total coliforms, and E. coli were the main risk factors in the studied region. Thus, this study contributes to strengthening water security by proposing a zonal management approach, providing support for implementing preventive strategies to protect water resources and public health.
Keywords:
risk analysis; probability and consequence; water quality
RESUMO
A análise de risco é amplamente reconhecida por sua simplicidade e eficácia na identificação e categorização de ameaças potenciais. Baseia-se em dois critérios essenciais, probabilidade e consequência, permitindo a priorização dos riscos e a adoção de medidas preventivas. Nesse contexto, o presente estudo aplica a metodologia de análise de probabilidade e consequência (P&C), combinada com a interpolação geoespacial, como ferramenta para a avaliação da qualidade da água e a garantia da segurança hídrica no município de Conceição do Araguaia, Pará. Para isso, as etapas metodológicas incluíram a identificação de parâmetros, coletas in loco, quantificação do risco, modelagem geoestatística e validação cruzada. Os resultados identificaram 19 pontos de amostragem classificados entre risco alto e crítico. A análise de validação revelou alta heterogeneidade local, demonstrando que a contaminação é governada por fatores microlocais. Apesar dessa variabilidade, o mapa mostrou-se eficaz na identificação de zonas macroscópicas de vulnerabilidade, otimizando a alocação de recursos ao concentrar as inspeções em aglomerados críticos. A análise demonstrou que os parâmetros pH, nitrito, coliformes totais e E. coli foram os principais fatores de risco na região estudada. Dessa forma, este estudo contribui para o fortalecimento da segurança hídrica ao propor uma abordagem de gestão por zonas, fornecendo subsídios para a implementação de estratégias preventivas voltadas à proteção dos recursos hídricos e da saúde pública.
Palavras-chave:
análise de risco; probabilidade e consequência; qualidade da água
INTRODUCTION
Safe drinking water is essential for human health, playing a vital role in maintaining biological balance and preventing diseases. Water contamination poses significant health risks, necessitating strict quality control measures to ensure its safety. International regulations emphasize the importance of continuous monitoring and the application of advanced treatment technologies to ensure that water intended for human consumption meets high safety standards (Kekes, Tzia and Kolliopoulos, 2023).
In Brazil, water quality regulations establish stringent guidelines for potability, covering organoleptic, microbiological, radioactive, and chemical aspects. These criteria encompass inorganic and organic substances, as well as contaminants such as pesticides, disinfectants, and disinfection by-products, ensuring compliance with public health standards (Araújo Junior and Teixeira, 2023). Ordinance GM/MS No. 888 defines approximately 130 quality parameters, organized into tables that set potability standards, while also regulating control and surveillance activities, which fall under the responsibility of service providers and municipal public health authorities (Brasil, 2021).
The presence of physical, chemical, and biological contaminants in drinking water can have severe consequences for human health, leading to gastrointestinal diseases and waterborne infections (Ezugwu and Akhimien, 2022). Research indicates that implementing rigorous water quality monitoring and control measures significantly reduces the incidence of illnesses associated with contaminated water consumption (Ngcongo and Tekere, 2023). Therefore, the adoption of effective public policies and innovative methodologies for continuous water security monitoring is crucial (Lopes et al., 2022).
Risk analysis plays a key role in improving water quality monitoring and management. Techniques such as Failure Mode and Effects Analysis (FMEA) help identify vulnerabilities in the supply system, enabling preventive actions before risks compromise water security (Araújo Junior and Teixeira, 2023). Additionally, risk-based surveillance is a proactive approach that enables more efficient resource allocation and mitigates negative impacts before they affect consumers (Lopes et al., 2022).
A complementary method to FMEA is Probability and Consequence (P&C) analysis, which offers an effective visual framework for risk prioritization, helping decision-makers focus on the most critical areas. This approach is applied during the risk assessment phase, where previously identified threats are analyzed to support strategic decision-making (Singh et al., 2024).
Given these considerations, this study aimed to apply P&C analysis as a tool to assess water quality and ensure water security in the municipality of Conceição do Araguaia, Pará. Specifically, the study sought to identify potential water quality risks in the region, conduct sampling in different neighborhoods to analyze key parameters, and develop a comprehensive risk matrix. This matrix integrates the findings of the P&C analysis, providing a clear visualization of major risks and supporting decision-making to mitigate impacts and enhance local water security.
METHOD
This study was conducted in the municipality of Conceição do Araguaia, Pará. It assessed drinking water quality and identified associated risks. The study was structured into seven key stages: identification of water quality parameters and criteria; on-site sampling; probability assessment; consequence assessment; risk quantification; risk classification; and the development of a municipal risk map.
In the first stage, water quality parameters and criteria were identified. The selected key parameters for evaluating water security in the municipality included color, turbidity, pH, free residual chlorine, nitrite, total dissolved solids (TDS), total coliforms, and E. coli. The quality criteria were based on the maximum allowable values (MAV) for each parameter, as established by Ordinance GM/MS No. 888/2021 (Brasil, 2021).
The second stage involved sample collection for subsequent laboratory analysis, where 46 sampling points were spatially distributed across the municipality’s urban area. All samples were collected in accordance with the National Guide for Sample Collection and Preservation (CETESB, 2011), and analyses followed the Standard Methods for the Examination of Water and Wastewater (APHA, AWWA and WEF, 2023). It is important to note that this dataset was also the basis for a preliminary hazard analysis using the Hazard Analysis and Critical Control Points (HACCP) method, as described by Araújo Junior et al. (2025). However, the present research offers a distinct scientific contribution by evolving from a process-oriented control (HACCP) to a strategic risk management framework using the P&C matrix, providing a new perspective on the severity and frequency of water quality risks.
In the third stage, the probability of exceeding water quality criteria was determined. Descriptive statistics were used to calculate the frequency of occurrence and corresponding probabilities.
For consequence assessment, the potential impacts of exceeding water quality criteria were analyzed for each parameter, considering public health effects and other relevant consequences.
The fifth stage involved risk quantification, integrating P&C analysis (Table 1) to determine the risk intensity for each parameter.
Considering the water quality parameters used, the scale proposed by Araújo Junior and Teixeira (2023) was adopted as the intensity scale, as shown in Figure 1.
Intensity scale for color, turbidity, pH, iron, free residual chlorine, fluorides, total coliforms, and E. coli.
It is important to note that Araújo and Teixeira (2023) did not include nitrite and TDS in their study parameters. Therefore, it was necessary to establish a scale for these parameters, as shown in Figure 2.
In this study, all water quality parameters were assigned equal weight in risk calculation. This decision is based on the normative framework of Ordinance GM/MS No. 888/2021, which establishes that any violation of MAV constitutes a non-compliance with drinking water standards. By adopting a normative approach instead of a subjective weighting scheme (such as those used in FMEA), the study ensures objectivity and replicability, treating every regulatory breach as a primary risk factor for water security.
In summary, probability was determined based on how frequently the parameters exceeded acceptable limits, calculated as the ratio between the number of non-compliant occurrences and the total number of samples. Consequence was defined according to the potential impact on public health, while intensity was assigned based on the severity of water quality standard deviation. The total risk at each sampling point was obtained by summing the individual risk values for each analyzed parameter, leading to the sixth stage: classifying water quality criticality into five levels (insignificant, low, moderate, high, and critical).
Finally, in the last stage, a water quality risk map was developed for the central area of Conceição do Araguaia using georeferenced risk interpolation through Kriging.
The application of Kriging interpolation was used to identify regional patterns of vulnerability and visualize spatial trends in water quality risk. While local heterogeneity is high, this probabilistic approach supports the prioritization of areas for intervention. While the preliminary study of this region (Araújo Junior et al., 2025) utilized the deterministic Inverse Distance Weighting (IDW) method, the present work advances by employing geostatistical Kriging.
The choice of the Kriging interpolation method for water quality risk mapping is justified by its capacity to model the spatial structure of the data, ensuring that estimates reflect the natural variability of environmental parameters more robustly than deterministic methods (Goovaerts, 2000). Kriging employs variograms to analyze spatial correlation, distinguishing it from methods such as IDW or polynomial interpolation (Li and Heap, 2014). This technique has been widely used in water quality studies due to its reliability in identifying risk zones, as seen in research analyzing contaminant distribution in water bodies (Leach and Coulibaly, 2020).
To assess the reliability of spatial interpolation, a Leave-One-Out Cross-Validation (LOOCV) technique was applied. This method consists of removing one sampling point at a time and using the remaining points to predict its value via Kriging. The performance of the model was evaluated using the Root Mean Square Error (RMSE) and the Coefficient of Determination (R2) to verify the coherence between observed and predicted risk values.
Finally, for the development of the risk map, a Geographic Information System (GIS) software was used, with risk classification ranging from 0 to 40, as shown in Table 2.
RESULTS AND DISCUSSION
Before presenting the risk analysis results themselves, it is important to highlight that the sampling design considered not only the homogeneous spatial coverage of the urban sector but also the geostatistical principles applied to Kriging interpolation. The literature (Belkhiri, Tiri and Mount, 2020; Goovaerts, 2000; Naz et al., 2024) establishes that the spacing between sampling points should be equal to or less than the variogram range of the parameters analyzed, ensuring that neighboring observations exhibit significant spatial correlation. When the spacing exceeds this range, interpolation essentially becomes extrapolation, thereby increasing the uncertainty of the map.
In this research, geostatistical analysis showed that the water quality parameters evaluated exhibit spatial autocorrelation at different magnitudes within the municipality. The estimated ranges varied from approximately 488 m for total coliforms and 486 m for TDS, to 609 m for E. coli, 1.09 km for apparent color, 1.43 km for pH, and 1.73 km for turbidity, reaching the highest value of around 4.95 km for nitrite, as shown in Table 3.
Considering that the average nearest-neighbor spacing in the sampling grid was about 217 m, it is observed that in all cases the estimated range is greater than the average distance between points, ensuring that the samples maintain spatial correlation within the study area.
Thus, considering that the variogram analysis suggested theoretical ranges greater than the average spacing (Table 3), the adopted sampling density was deemed sufficient to ensure representativeness for the application of Kriging, following standard geostatistical principles (Goovaerts, 1997; Isaaks and Srivastava, 1989). However, the cross-validation of the integrated Risk Map revealed a dominant ‘nugget effect’ at short distances. This indicates that while sampling density was adequate to identify macroscopic zones of vulnerability (neighborhood level), water quality variability is strongly influenced by micro-local factors (e.g., individual septic tank proximity) occurring at a scale smaller than the sampling grid.
In addition to the geostatistical analysis, it is noteworthy that the number of sampling points used in this study (46 points) exceeds both national and international regulatory recommendations. Ordinance GM/MS No. 888/2021 establishes, for municipalities with a population similar to that of Conceição do Araguaia, — approximately 44,600 urban inhabitants, according to the latest census data (IBGE, 2022) — a requirement of 44~45 monthly samples (1 per 1,000 inhabitants), while World Health Organization (WHO) guidelines recommend a similar sampling frequency (approximately 1 sample per 5,000 to 10,000 inhabitants/month) (WHO, 2011). It is important to note, however, that the present study focused on only a specific portion of the municipality’s urban area.
Based on analyses of the 46 samples, and following the parameters established by Ordinance GM/MS No. 888/2021, occurrences of parameters exceeding the MAV set by the ordinance were identified. Among the non-compliant parameters, the most notable were apparent color, turbidity, pH, nitrite, total coliforms, and E. coli. The frequency of each parameter is shown in Table 4.
To classify the consequence, the following aspects were considered for each parameter, focusing on the most frequently occurring ones: apparent color, which affects taste and odor. Although it is an aesthetic parameter that does not necessarily pose a direct health risk, it may be associated with the presence of organic matter. Turbidity is linked to the risk of waterborne diseases such as cholera and giardiasis. pH variations can cause irritation, taste and odor alterations, and digestive issues. Nitrite exposure can lead to methemoglobinemia, also known as “blue baby syndrome,” in addition to causing acute toxicity, resulting in nausea, vomiting, abdominal cramps, and diarrhea. Total coliforms and Escherichia coli are associated with gastrointestinal illnesses, dehydration, and other severe infections. The consequences for all parameters are listed in Table 5.
By combining the probability of a parameter exceeding the MAV established by Ordinance GM/MS No. 888/2021 with the potential consequences for human health, the following results were obtained regarding the degree of risk they represent. The highest-risk parameters identified were pH, nitrite, E. coli, and total coliforms, as shown in Table 6.
According to the risk matrix, the apparent color parameter was classified as an insignificant risk, while free residual chlorine and turbidity were categorized as low risk. Total dissolved solids and the presence of E. coli were classified as medium risk, whereas nitrite and total coliforms were identified as critical risks.
After quantifying the risks for each of the 46 georeferenced points, a water quality risk map was generated for the central area of the municipality of Conceição do Araguaia, as shown in Figure 3.
The reliability of the final Risk Map was assessed using LOOCV. The results yielded a RMSE of 7.20 and a negative Coefficient of Determination (R2 of -0.13). These metrics quantitatively confirm the high spatial heterogeneity of the aquifer in the urban area. The low R2 indicates that the risk level of a specific well is poorly predicted by its neighbors, supporting the hypothesis that contamination is driven by point-sources rather than diffuse regional plumes. Consequently, the map in Figure 3 should be interpreted as a zonal guide for public management prioritization, identifying clusters of vulnerability, rather than a deterministic predictor for specific unmonitored coordinates.
Complementing the risk assessment, an uncertainty map was generated based on the Kriging variance. This map identifies areas where the prediction error is potentially higher due to the lower density of sampling points. These zones of high uncertainty indicate priority locations for future sampling campaigns to refine model accuracy.
The analysis of the risk map revealed that of the 46 sampling points, 19 exhibited a risk level classified between high and critical (14 high and 5 critical). Unlike a concentrated central plume, these critical points are distributed throughout the urban area, identifying focal points of vulnerability in the northern, central, and southern sectors. In these zones, risk values range from 24.25 to 34.11, requiring priority attention. In contrast, the northern and southern areas of the municipality predominantly exhibit negligible to moderate risks, suggesting better water quality.
Although identifying the specific causes is not the focus of this study, it is important to highlight that, according to Arruda et al. (2023), the municipality lacks a sewage collection system and wastewater treatment, which may contribute to groundwater contamination, especially in areas identified as highly vulnerable in the risk matrix. The majority of the sampling points rely on wells, making groundwater more susceptible to contaminant infiltration, a risk exacerbated by the disposal of domestic sewage in rudimentary cesspits. Additionally, the proximity of a cemetery to one of these critical zones raises further concerns, as studies indicate that cemeteries can contribute to groundwater contamination through nitrogen compounds and other pollutants (Nesheim et al., 2024; Ponce-Arguello et al., 2024). This scenario reinforces the importance of the methodology used. It enables the spatialization of risks and can support future investigations into the relationship between water quality and the absence of adequate sanitation infrastructure.
However, it is important to consider the influence of rainfall regimes and seasonal hydrological cycles on water quality in Amazonian regions. Studies such as those by Prado et al. (2021) and Guedes et al. (2024) show that rainfall seasonality significantly impacts the physicochemical and microbiological parameters of water. During the rainy season, there is an increase in turbidity, color, and nitrate from the runoff of sediments and nutrients. On the other hand, in the drier season, reduced water volume and lower contaminant dilution lead to higher concentrations of substances such as ammonia and phosphate, intensifying eutrophication processes (Silva, Robrini and Freitas, 2022). Although the current risk map reflects a specific sampling period, providing a localized ‘snapshot’, these seasonal cycles must be considered as a potential factor for temporal risk variation. Therefore, future studies incorporating longitudinal data are recommended to validate these seasonal patterns and evaluate how the identified critical zones fluctuate throughout the year.
The spatial identification of water quality risks through the P&C matrix and Kriging interpolation represents a significant advancement in optimizing strategic actions for water supply companies and regulatory agencies responsible for water quality monitoring and control. This approach enables more efficient resource allocation, directing efforts toward high vulnerability zones. Instead of random monitoring, resources can be focused on these critical clusters, improving public health risk management and response to potential contamination scenarios.
Although Kriging interpolation is widely used for analyzing water quality parameters, it is important to emphasize that this method has limitations. Hassan et al. (2023) point out that Kriging assumes the variables to be interpolated follow a stationary stochastic process, which implies that the spatial variability properties of the parameter remain constant throughout the study area. This assumption does not always apply to all scenarios, especially in regions with large environmental variations or with sampling data that are sparse and irregularly distributed (Belkhiri, Tiri and Mount, 2020). Nevertheless, despite these limitations, Kriging remains a robust and effective method for analyzing water quality parameters, successfully capturing spatial variations, particularly when combined with other methods (Belkhiri, Tiri and Mount, 2020; Tao et al., 2025). However, risk analysis can also be addressed through alternative methodologies, such as P&C, HACCP, or FMEA, which differ in scope and application.
The P&C method differs from other risk analysis approaches applied to water security. For example, a study conducted in Belém-PA used the FMEA methodology to assess risks at 46 points in the supply system, including treatment plants, reservoirs, and distribution networks (Araújo Junior and Teixeira, 2023). However, this approach focused on identifying failures at specific system points (treatment plants, reservoirs, and networks). In contrast, the present study contributes by providing a detailed geospatial perspective of the risks exclusively at consumption points, using interpolation to map the most vulnerable areas.
Similarly, the HACCP methodology, applied in Larache, Morocco, was used to monitor drinking water quality, emphasizing preventive control of critical points in the supply system (El Attaoui, Sossi and El Khatori, 2023). While effective in preventing failures in treatment processes, this approach does not quantify or spatially represent risks. Meanwhile, the barrier-based analysis adopted in the United Kingdom focuses on preventing structural failures in the distribution system (Walker, 2023), whereas the present study identifies specific regions requiring urgent intervention.
Additionally, water quality risk prediction can benefit from advanced statistical models, as demonstrated in a study conducted in China, which applied copula-based Bayesian networks to predict seasonal variations and identify critical environmental factors (Yu and Zhang, 2021). Although this model enables detailed forecasts based on climatic and seasonal factors, the present study stands out for its ability to assess the real-time spatial distribution of risk, providing valuable information for immediate water quality management.
Table 7 presents a comparison of these approaches, highlighting their key characteristics and differences from the present study.
The key advantage of the P&C methodology combined with georeferenced interpolation, as proposed in this study, lies in its ability to quantify and spatially visualize risks, an aspect not always covered by other methods. This distinction allowed for the precise identification of areas most vulnerable to water contamination, optimizing water quality management.
Future studies may integrate different risk analysis methodologies, such as P&C and FMEA, as this combination would merge the precision of identifying operational and structural failures (FMEA) with the predictive and spatial capabilities of P&C. This integrated approach would provide a more comprehensive risk assessment for water supply systems, enabling the anticipation of critical scenarios and enhancing water security management.
This study presents some limitations that should be acknowledged. First, the absence of sampling during different seasons of the year prevents a more comprehensive assessment of seasonal and temporal influences on water quality. Second, the cross-validation process highlighted the complexity of modeling urban aquifers with high constructive variability of wells. The validation metrics suggest that future monitoring programs should consider higher sampling density in the identified critical zones to capture the micro-scale variability.
Beyond methodological aspects, the risk classification was based on discrete scales of the P&C matrix, which inherently introduces a degree of subjectivity in interval definitions. However, the study assigned equal weights to all parameters to comply with Ordinance GM/MS No. 888/2021. While other methodologies, such as FMEA, allow for differentiated weighting to reflect varying health severities, such approaches often increase subjectivity by requiring expert consensus. By prioritizing a normative scale, this model enhances replicability for public health monitoring, as it treats any violation of drinking water standards as a critical risk factor. Furthermore, although it was not the main objective of this study, proposing technical or policy measures for the critical zones identified is a fundamental step toward the practical application of the model and its validation in terms of socio-environmental impacts. Likewise, a more in-depth quantitative analysis of the contribution of specific contamination sources is needed.
Finally, another relevant aspect concerns the economic evaluation. While the use of key water quality parameters to determine risks and the spatial interpolation of adjacent areas appears promising for reducing monitoring costs and supporting decision-making — theoretically increasing cost-effectiveness — it is essential to advance the economic analysis to confirm this hypothesis more consistently.
Despite these limitations, this study makes an important contribution by integrating the P&C matrix with geostatistical modeling to assess drinking water quality risks. Building on these findings, several mitigation strategies and public policy measures can be proposed for the critical zones identified. First, the protection of water sources and aquifer recharge areas is essential, with restrictions on land uses that increase the risk of contaminant infiltration. This aligns with the preventive approach recommended by the WHO (WHO, 2006), especially through the adoption of Water Safety Plans (WSPs), which incorporate risk assessments from catchment to consumer (Lindhé et al., 2013). Quantitative risk matrices and geospatial modeling have been successfully applied in urban water systems to identify vulnerable points and prioritize interventions (Meshram et al., 2023). Furthermore, adaptive monitoring strategies — such as increased sampling density in high-risk areas and systematic cross-validation of Kriging models — can improve predictive accuracy and enhance the allocation of resources for risk mitigation (Golaki et al., 2024; Raju et al., 2019).
Another relevant measure is community engagement and health education, encouraging the proper use of septic systems, regular well maintenance, and safe waste disposal practices, all of which are crucial to prevent groundwater contamination. Structured sanitary survey methods have proven effective in identifying and communicating localized contamination risks and engaging communities in mitigation actions (Baker et al., 2016). Finally, integrating these measures into municipal health and sanitation plans can help prioritize investments in sanitation infrastructure and reinforce epidemiological surveillance systems. The international literature supports these strategies as feasible and effective approaches to reducing water contamination risks and strengthening water governance, particularly in structurally vulnerable municipalities (Lindhé et al., 2013).
CONCLUSIONS
This study demonstrated that the P&C matrix combined with Kriging interpolation is a valuable tool for identifying and spatially mapping water quality risks. The validation process, however, highlighted the high local heterogeneity of the aquifer, indicating that contamination risks are driven by micro-local factors rather than broad regional plumes.
This approach enables a more efficient allocation of resources by directing strategic actions to specific vulnerability clusters. The analysis conducted in the municipality of Conceição do Araguaia, PA, identified 19 sampling points with risk levels classified between high and critical, distributed across different sectors of the city, highlighting the need for continuous monitoring and the implementation of preventive measures focused on sanitary improvements at the household level.
Furthermore, the results indicate that pH, nitrite, total coliforms, and E. coli were the primary risk factors in the study area. The proposed approach distinguishes itself from traditional methodologies, such as FMEA and HACCP, as it not only classifies risks but also visualizes their spatial distribution. Although the geostatistical validation indicated limitations in predicting exact values between sampling points due to the ‘nugget effect’, the resulting map successfully serves as a zonal management guide, facilitating decision-making and promoting water security.
Thus, this study reinforces the effectiveness of integrating risk analysis into water quality assessment as a complementary strategy. Future studies should consider increasing sampling density in the critical zones identified to capture micro-scale variability and explore the combination of P&C with predictive statistical models to enhance the forecasting of critical scenarios.
DATA AVAILABILITY STATEMENT
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
REFERENCES
-
ALI, Abdalaal Hamad; KEHAIL, Mutaman Ali Abdelgadir; ELDAEM, Bakri Yousif Nour. Evaluation of coliform (E. coli) load of the drinking water collected from Kosti and Rabak Cities, White Nile State, Sudan. International Journal of Science and Research Archive, v. 11, n. 2, p. 1804–1808, 2024. https://doi.org/10.30574/ijsra.2024.11.2.0685
» https://doi.org/10.30574/ijsra.2024.11.2.0685 - AMERICAN PUBLIC HEALTH ASSOCIATION - APHA; AMERICAN WATER WORKS ASSOCIATION - AWWA; WATER ENVIRONMENT FEDERATION - WEF. Standard Methods for the Examination of Water and Wastewater 24. ed. Washington: APHA Press, 2023. 1516 p.
-
ARAÚJO JÚNIOR, Antonio Jorge Silva; LEAL, Ana Paula Brandão; ARAÚJO, Eva Silvestre; ARRUDA, Joel Noleto; ALVES, Mirian Santos; COSTA, Carlos Eduardo Aguiar de Souza. Análise de perigos e pontos críticos de controle (APPCC) no monitoramento de qualidade da água em uma cidade amazônica. Territorium Terram, v. 8, n. 14, p. 1-21, 2025. https://doi.org/10.5281/zenodo.14562243
» https://doi.org/10.5281/zenodo.14562243 -
ARAÚJO JÚNIOR, Antonio Jorge Silva; TEIXEIRA, Luiza Carla Girard Mendes. Utilização da análise de riscos como ferramenta para segurança da qualidade da água. Periódico Eletrônico Fórum Ambiental da Alta Paulista, v. 19, n. 4, 2023. https://doi.org/10.17271/1980082719420233541
» https://doi.org/10.17271/1980082719420233541 -
ARHIN, Emmanuel; OSEI, Jeff Dacosta; ANIMA, Prisca Ama; DAMOAH-AFARI, Peter; YEVUGAH, Lily Lisa. The pH of Drinking Water and Its Human Health Implications: A Case of Surrounding Communities in the Dormaa Central Municipality of Ghana. Journal Healthcare Treatment Development, n. 41, p. 15–26, 2023. https://doi.org/10.55529/jhtd.41.15.26
» https://doi.org/10.55529/jhtd.41.15.26 -
ARRUDA, Joel Noleto; ARAÚJO, Eva Silvestre; LEAL, Ana Paula Brandão; OLIVEIRA, Kariny Silva de; ARAÚJO JUNIOR, Antonio Jorge Silva; MOREIRA, Francisca Nara da Conceição; CORRÊA, Marina Scarano. Monitoramento da qualidade da água do rio Araguaia em Conceição do Araguaia-PA: caracterização do manancial em termos de qualidade. Brazilian Journal of Development, v. 9, n. 05, p. 15507–15516, 2023. https://doi.org/10.34117/bjdv9n5-070
» https://doi.org/10.34117/bjdv9n5-070 -
BAKER, Danielle; FERGUSON, Christobel Margaret; CHIER, P.; WARNECKE, Malcolm; WATKINSON, Andrew. Standardised survey method for identifying catchment risks to water quality. Journal of Water and Health, v. 14, n. 3, p. 349-358, 2016. https://doi.org/10.2166/wh.2015.054
» https://doi.org/10.2166/wh.2015.054 -
BELKHIRI, Lazhar; TIRI, Ammar; MOUNI, Lotfi. Spatial distribution of the groundwater quality using kriging and Co-kriging interpolations. Groundwater for Sustainable Development, v. 11, p. 100473, 2020. https://doi.org/10.1016/j.gsd.2020.100473
» https://doi.org/10.1016/j.gsd.2020.100473 -
BRASIL. Ministério da Saúde. Portaria GM/MS no 888, de 4 de maio de 2021. Altera o Anexo XX da Portaria de Consolidação GM/MS nº 5, de 28 de setembro de 2017, para dispor sobre os procedimentos de controle e de vigilância da qualidade da água para consumo humano e seu padrão de potabilidade. Diário Oficial da União, Brasília, 4 maio 2021. Available at: https://www.in.gov.br/en/web/dou/-/portaria-gm/ms-n-888-de-4-de-maio-de-2021-318461562 Accessed on: Sept. 7, 2024.
» https://www.in.gov.br/en/web/dou/-/portaria-gm/ms-n-888-de-4-de-maio-de-2021-318461562 - COMPANHIA AMBIENTAL DO ESTADO DE SÃO PAULO – CETESB. Guia Nacional de Coleta e Preservação de Amostras: Água, Sedimento, Comunidades Aquáticas e Efluentes Líquidos. São Paulo: CETESB/ANA, 2011.
-
EL ATTAOUI, Zuhair; SOSSI, Fatima Zohra Alaoui; EL KHATORI, Youssef. Risk management for improving water quality: Application of the HACCP method. E3S Web of Conferences, v. 412, p. 01050, 2023. https://doi.org/10.1051/e3sconf/202341201050
» https://doi.org/10.1051/e3sconf/202341201050 -
EZUGWU, Maryann Ogoamaka; AKHIMIEN, Faith Osarumwense. Review of the Effects of Water Characteristics and Quality on Human Health. International Journal of Current Science Research and Review, v. 5, n. 03, 2022. https://doi.org/10.47191/ijcsrr/V5-i3-09
» https://doi.org/10.47191/ijcsrr/V5-i3-09 -
FISH, Katherine; REEVES-MCLAREN, Nik; HUSBAND, Stewart; BOXALL, Joby. Uncharted waters: the unintended impacts of residual chlorine on water quality and biofilms. npj Biofilms and Microbiomes, v. 6, n. 1, p. 34, 2020. https://doi.org/10.1038/s41522-020-00144-w
» https://doi.org/10.1038/s41522-020-00144-w -
GOLAKI, Mohammad; GHAREHCHAHI, Ehsan; MAHMOUDI, Norouz; RASHIDI, Majid; AZHDARPOOR, Abooalfazl. Assessing water quality of Kazerun County in southwest Iran: Multi-analytical techniques, deterministic vs. probabilistic water quality index, geospatial analysis, fuzzy C-means clustering, and machine learning. Groundwater for Sustainable Development, v. 27, n. 45, 101336, 2024. https://doi.org/10.1016/j.gsd.2024.101336
» https://doi.org/10.1016/j.gsd.2024.101336 - GOOVAERTS, Pierre. Geostatistics for natural resources evaluation New York: Oxford University Press, 1997. 483 p.
-
GOOVAERTS, Pierre. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, v. 228, n. 1–2, p. 113–129, 2000. https://doi.org/10.1016/S0022-1694(00)00144-X
» https://doi.org/10.1016/S0022-1694(00)00144-X - GUEDES, Jimaine Nascimento; BRITO, Daímio Chaves; MALCHER, José Angelo Simões; CUNHA, Alan Cavalcanti da; SILVA, Gabriel Araújo da. Influência sazonal na qualidade da água e salinização na foz do Rio Amazonas/AP. Planeta Amazônia: Revista Internacional de Direito Ambiental e Políticas Públicas, n. 16, p. 41–58, 2024.
-
HASSAN, Haider; ISMAEEL, Abaas; ETHAIB, Saleem; Al-ZAIDI, Basim. Developing Spatial Models of Groundwater Quality in the Southwestern Desert of Iraq Using GIS, Inverse Distance Weighting, and Kriging Interpolation Techniques. Mathematical Modelling of Engineering Problems, v. 10, n. 4, p.1169–1179, 2023. https://doi.org/10.18280/mmep.100409
» https://doi.org/10.18280/mmep.100409 -
HWANG, Bing-Fang; MAGNUS, Per; JAAKKOLA, Jouni. Risk of Specific Birth Defects in Relation to Chlorination and the Amount of Natural Organic Matter in the Water Supply. American Journal of Epidemiology, v. 156, n. 4, p. 374–382, 2002. https://doi.org/10.1093/aje/kwf038
» https://doi.org/10.1093/aje/kwf038 -
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA - IBGE. Censo 2022. Conceição do Araguaia-PA. IBGE, 2022. Available at: https://cidades.ibge.gov.br/brasil/pa/conceicao-do-araguaia.html Accessed on: Sept. 22, 2025.
» https://cidades.ibge.gov.br/brasil/pa/conceicao-do-araguaia.html - ISAAKS, Edward; SRIVASTAVA, R. Mohan. An introduction to applied geostatistics New York: Oxford University Press, 1989. 561 p.
-
KEKES, Tryfon; TZIA, Constantina; KOLLIOPOULOS, Georgios. Drinking and Natural Mineral Water: Treatment and Quality–Safety Assurance. Water, v. 15, n. 13, p. 2325, 2023. https://doi.org/10.3390/w15132325
» https://doi.org/10.3390/w15132325 -
LEACH, James; COULIBALY, Paulin. Soil moisture assimilation in urban watersheds: A method to identify the limiting imperviousness threshold based on watershed characteristics. Journal of Hydrology, v. 587, p. 124958, 2020. https://doi.org/10.1016/j.jhydrol.2020.124958
» https://doi.org/10.1016/j.jhydrol.2020.124958 -
LI, Jin; HEAP, Andrew. Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, v. 53, p. 173–189, 2014. https://doi.org/10.1016/j.envsoft.2013.12.008
» https://doi.org/10.1016/j.envsoft.2013.12.008 -
LINDHE, Aandreas; ROSÉN, Lars; RØSTUM, Jon; NORBERG, Tommy; PETTERSSON, Thomas. Uncertainty modelling in multi-criteria analysis of water safety measures. Environment Systems and Decisions, v. 33, n. 2, p. 195–208, 2013. https://doi.org/10.1007/s10669-013-9442-9
» https://doi.org/10.1007/s10669-013-9442-9 -
LOPES, Rayssa Horacio; SILVA, Cicera Renata Diniz Vieria; SALVADOR, Pétala Tuani Cândido de Oliveira; SILVA, Ísis de Siqueira; HELLER, Léo; UCHÔA, Severina Alice da Costa. Surveillance of Drinking Water Quality Worldwide: Scoping Review Protocol. International Journal of Environmental Research and Public Health, v. 19, n. 15, p. 8989, 2022. https://doi.org/10.3390/ijerph19158989
» https://doi.org/10.3390/ijerph19158989 -
MATSUMOTO, Yoshinari; MORI, Hijiri; HAYAKAWA, Akemi; OHASHI, Masaru. Influence of free residual chlorine on cultured human epidermal keratinocytes from normal skin and hypertrophic scars. Journal of Dermatological Science, v. 10, n. 1, p. 1–7, 1995. https://doi.org/10.1016/0923-1811(94)00381-N
» https://doi.org/10.1016/0923-1811(94)00381-N -
MESHRAM, Pavin; SARGAONKAR, Aabha; KAMBLE, Swapnil; INDURKAR, Shreya; MESHRAM, Vaishali; THAKRE, Mahendra. Risk assessment of urban drinking water supply system: a pilot scale study in central India. International Journal of Researches in Biosciences, Agriculture and Technology, v. 11, n. 1, p. 206–218, 2023. http://doi.org/10.29369/ijrbat.2023.010.1.0034
» https://doi.org/10.29369/ijrbat.2023.010.1.0034 -
MORTADA, Wael; SHOKEIR, Ahmed. Does nitrite and nitrate levels in drinking water impact the health of people in Dakahlia governorate, Egypt?. Environmental Science and Pollution Research, v. 25, n. 20, p. 19728–19738, 2018. https://doi.org/10.1007/s11356-018-2156-2
» https://doi.org/10.1007/s11356-018-2156-2 - MUNJAL, Shikha; SINGH, Aakash; MEHRA, Deepak Kumar; KUMARI, Pooja. Effect of TDS on Human Immune System. International Journal of Innovative Research and Growth, v. 8, n. 2, p. 6–10, 2018.
- NATIONAL HEALTH AND MEDICAL RESEARCH COUNCIL. Australian drinking water guidelines 6 [S.l.]: National Health and Medical Research Council, 2022.
-
NAZ, Iram; AHMAD, Ijaz; ASLAM, Rana Waqar; QUDDOOS, Abdul; YASEEN, Andaleeb. Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices. Water, v. 16, n. 1, e63, 2024. https://doi.org/10.3390/w16010063
» https://doi.org/10.3390/w16010063 -
NESHEIM, Samuel; YU, Zhongjie; TUTTLE, Samuel; KLEIN, Jenna; WEN, Tao. Assessing impacts of cemeteries on water quality in an urban headwater watershed with mixed human-built infrastructure. Hydrological Processes, v. 38, n. 3, e15128, 2024. https://doi.org/10.1002/hyp.15128
» https://doi.org/10.1002/hyp.15128 -
NGCONGO, Magareth Thulisile; TEKERE, Memory. WASH and drinking water quality considerations in schools in reflection of the sustainable development goals – a review. Journal of Water, Sanitation and Hygiene for Development, v. 13, n. 8, p. 566–583, 2023. https://doi.org/10.2166/washdev.2023.028
» https://doi.org/10.2166/washdev.2023.028 -
PONCE ARGUELLO, Mariuxi; CRISANTO-PERRAZO, Tania; VIZUETE, Diego; OCAÑA GARZÓN, Edwin; GUEVARA GARCIA, Paulina; ALDÁS, Maria Belén; JARAMILLO, Stephany; TOULKERIDIS, Theofilos. Evaluation of Water Contamination Caused by Cemeteries in Central Ecuador – A Warning for the Authorities. Water, v. 16, n. 16, p. 2310, 2024. https://doi.org/10.3390/w16162310
» https://doi.org/10.3390/w16162310 - PRADO, Joici Rodrigues Silva; FIGUEIREDO, Daniela Maimoni de; DORES, Eliana Freire Gaspar de Carvalho; HONGYU, Kuang; DELFINO, Aquila José Gonçalves; RODRIGUES, Daniel Victor Brito. Variação da qualidade da água em relação à sazonalidade e ao uso da terra no sul da região amazônica. Caderno Prudentino de Geografia, v. 43, n. 2, p. 159–184, 2021.
-
RAJU, Ashwani; SINGH, Anjali; SRIVASTAVA, Nupur; SINGH, Sandeep; JIGYASU, Dharmendra Kumar; SINGH, Munendra. Mapping human health risk by geostatistical method: a case study of mercury in drinking groundwater resource of the central Ganga alluvial plain, northern India. Environmental Monitoring and Assessment, v. 191, Suppl. 2, p. 298, 2019. https://doi.org/10.1007/s10661-019-7427-y
» https://doi.org/10.1007/s10661-019-7427-y -
SILVA, Rafael Oliveira da; ROBRINI, Maamar El; FREITAS, Margarida Pereira de. Influência do índice de chuvas e de rejeitos de efluentes na qualidade das águas do igarapé Santos (Tucuruí – Sudeste Paraense/Amazônia Oriental). Boletim Paulista de Geografia, n. 108, p. 188–209, 2022. https://doi.org/10.54446/bpg.v109i1.2953
» https://doi.org/10.54446/bpg.v109i1.2953 -
SINGH, Garima; JAHAN, Aarzoo; KALRA, Rupinder; JOSHI, Arpita; SUKHWINDER; SARIN, Namrata; SINGH, Sompal; GUPTA, Ruchika. Risk management in a clinical pathology laboratory: Application of brainstorming and probability analysis for risk reduction. Indian Journal of Pathology and Microbiology, v. 67, n. 3, p. 607–610, 2024. https://doi.org/10.4103/ijpm.ijpm_262_22
» https://doi.org/10.4103/ijpm.ijpm_262_22 -
STEVENSON, Matthew; BRAVO, Cristián. Advanced turbidity prediction for operational water supply planning. Decision Support Systems, v. 119, p. 72–84, 2019. https://doi.org/10.1016/j.dss.2019.02.009
» https://doi.org/10.1016/j.dss.2019.02.009 -
TAO, Baoping; HAN, Zifei; SHI, Wen; WANG, Min; OUYANG, Linhan. Bayesian variable selection in Kriging metamodeling for quality design. European Journal of Operational Research, v. 328, n. 1, p. 216–231, 2025. https://doi.org/10.1016/j.ejor.2025.06.003
» https://doi.org/10.1016/j.ejor.2025.06.003 -
WALKER, Richard. Misjudging drinking water quality risk: Adopting a barrier approach for meaningful risk assessment to address latent risks. Journal of Water and Health, v. 21, n. 10, p. 1404–1420, 2023. https://doi.org/10.2166/wh.2023.077
» https://doi.org/10.2166/wh.2023.077 - WORLD HEALTH ORGANIZATION - WHO. Protecting Groundwater for Health: Managing the Quality of Drinking-water Sources. London: IWA Publishing, 2006. 220 p.
-
WORLD HEALTH ORGANIZATION -WHO. Guidelines for drinking-water quality 4. ed. Geneva: World Health Organization, 2011. 564 p. Available at: https://apps.who.int/iris/handle/10665/44584 Accessed on: Sept. 22, 2025.
» https://apps.who.int/iris/handle/10665/44584 -
YU, Ruolan; ZHANG, Chen. Early warning of water quality degradation: A copula-based Bayesian network model for highly efficient water quality risk assessment. Journal of Environmental Management, v. 292, p. 112749, 2021. https://doi.org/10.1016/j.jenvman.2021.112749
» https://doi.org/10.1016/j.jenvman.2021.112749 -
ZARIĆ, G.; COCOLI, Sonila; ŠARČEVIĆ, D.; VJEŠTICA, Suncica; PRODANOVIĆ, Radivoj; PUVAČA, Nikola; CARIĆ, Marko. Escherichia coli as Microbiological Quality Water Indicator:A High Importance for Human and Animal Health. Journal of the Hellenic Veterinary Medical Society, v. 74, n. 3, p. 6117–6124, 2023. https://doi.org/10.12681/jhvms.30878
» https://doi.org/10.12681/jhvms.30878
Edited by
-
Editor:
Maurício Alves da Motta Sobrinho https://orcid.org/0000-0003-2638-9096
Source: 
Source: Authors, 2025.
Source: Authors, 2025.