Multi-scale trend analysis of water quality using error propagation of generalized additive models

In Brazil, specifically in the Doce River basin, there is still a great lack of studies on temporal and spatial trends in water quality, since the water quality in the monitoring campaigns is basically evaluated when it comes down to the concentrations of monitored variables. In this sense, the objective of this work was to perform a temporal and spatial trend analysis of water quality data in the Minas Gerais portion of the Doce River basin, Brazil. For this, the Mann–Kendall, seasonal Mann–Kendall and Spearman correlation tests were used in the temporal analysis and the cluster analysis in the spatial analysis. In the analysis of temporal trends, the analyses were performed using the values of the National Sanitation Foundation Water Quality Index (NSFWQI) and the variables that compose it. In the analysis of spatial trends, the stations were evaluated only based on the WQI. With the results of the analysis of temporal trend, it was identified that most stations did not present a statistically significant trend for the WQI. In the stations that presented trends of quality reduction, most of them are in densely populated areas, demonstrating the strong influence of the poor sanitary conditions of the municipalities to the water quality of the basin. When analyzing the variables that compose the WQI, the results found for nitrate demonstrated that water quality deterioration is also affected by the diffuse pollution originating from farming areas. The results for Escherichia coli reinforced the impact of the discharge of domestic effluents and demonstrated the absence of a significative trend is still of concern because it can represent the maintenance of a degradation state in the water bodies. In the spatial trend analysis, the CA grouped the monitoring stations into six clusters based on their similarity among the WQI values, and, together with the results of the other analyses, it was verified that the Caratinga River basin (UGRH5 Caratinga) presented the highest degree of pollution. It was also possible to identify five stations that can be reallocated or deactivated since they have similarities with other stations located in the same watercourse.

Use of water quality indices to verify the impact of Córdoba City (Argentina) on Suquı́a River

In this paper, spatial and temporal pattern analysis was performed to obtain better understanding of the water quality in the Min River Basin. The water quality data of 34 sites for five indicators, Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD 5 ), Total Nitrogen (TN), Total Phosphorus (TP), and Dissolved Oxygen (DO) between 2001 and 2010 were analyzed. The spatial trends of water quality for the 2000s were presented. An interpolation approach based on the distance along river network was used to reveal the spatial variation of water quality in year 2010. A non-parametric seasonal Mann-Kendall's test was employed to determine the significance of temporal trends for each parameter of each site. Basically, the water quality of the Min River basin is good and most of the segments can satisfy the national environmental standards of Class III. Moreover, certain spatial and temporal patterns can be discovered through the analysis. The best water quality is in the Dadu River, the Qingyi River, and the upper parts of the Min River. The water quality of the Fu River is the worst in the basin. The middle parts of the Min River are worse than the lower parts and better than the Fu River. From temporal perspective, COD showed significant upward trend, whereas BOD 5 showed significant downward trend. TN and TP showed increasing trends in a similar spatial manner, which exhibits the signs of degradation in water quality. Particularly, the concentration of TN exhibited significant upward trend. Nevertheless, DO showed some upward trends. The reasons of the spatial-temporal characteristics were also discussed and some water management strategies were recommended.

Evaluation of water quality and potential scaling of corrosion in the water supply using water quality and stability indices: A case study of Juja water distribution network, Kenya

水资源短缺是全球性关注的问题,水质恶化更加剧了这一问题的严重性,定量研究水质变化及其影响因素可为治理水环境提供基础依据.与实测数据及综合污染指数的对比表明,水质指数能够合理反映水质的变化程度和时空变化趋势.利用西苕溪流域1996-2000年水质监测数据的研究结果如下:西苕溪流域水质的空间变化趋势是自上游至下游逐渐恶化,时间变化的总体趋势是逐年转好;点污染源得到有效控制年份(1999年)的水质指数比以前年份(1996-1998年)有所提高但幅度不大,说明非点源污染是影响西苕溪流域水质的重要因素;流域水污染的主要形式是氮、磷污染,其主要非点源是农田、经济竹林和城镇径流及居民生活污水等.;The scare of fresh water has been becoming a focus issue in the world, while water quality deterioration enhances the seriousness of the problem. To assess the water quality and its impact factors can provide basic information for water resource management. This paper verified the usefulness of water quality indices (WQI) to assess the water quality from multiple measured parameters, and to evaluate the spatial and temporal change. The WQI which takes into consideration all the measured parameters not only shows a similar trend to the results of analyzing separate parameters and the index of compositive pollution, but also display the overall water quality and can predict precisely the change trend of water quality. As a case study, we use the WQI to evaluate spatial and temporal changes in the water quality from Xitiaoxi Catchment, upper reaches of Taihu Basin, and assess the degree of factors influencing water quality. The nonpoint source pollution caused by land use produces a serious and negative effect on the water quality because only controlling and management point sources only increase 6.5% of water quality. The water quality gradually deteriorated along the river from upper reaches to lower reaches mainly due to the land use change. So, in the future, the highest priority measure to improve water quality of Xitiaoxi Catchment should be controlling non-point pollution resources.

Using water quality indices (WQIs) is one way to monitor watercourses for water quality. Currently, there is a lack of information about their behavior in the diurnal cycle because sampling is often carried out in the morning. Also, few articles focus on assessing the urban impact on the spatial variability of WQIs in tropical first-order streams. Such streams receive many pollutants varying in intensity according to population habits, justifying the possible diurnal variation in water quality in addition to climatic attributes. This paper aims to evaluate the fluctuations in Brazilian WQIs and their parameters (temperature, turbidity, total solids [TS], hydrogen potential, dissolved oxygen [DO], biochemical oxygen demand [BOD], total phosphorus [TP], total nitrogen [TN], and Escherichia coli) between the morning (8 AM), afternoon (2 PM), and night (7 PM) periods for an urban first-order tropical stream. Overall, the lowest DO concentrations and highest values of TS, turbidity, BOD, TP, TN, and E. coli were obtained in the morning, possibly representing population habits: the greatest generation of pollutants occurs overnight and in the early morning because there are clandestine domestic wastewater inputs into the stream, whose hourly periodicity generates a similar periodicity in the WQI of the evaluated stream. Although there was a significant variation in WQI average values between morning (15.50 ± 1.97) and afternoon (20.83 ± 5.42) only during the dry season (p < 0.05), different results were common throughout the day in all months, and the water quality was often classified in distinct categories: very bad, bad, and regular. Our findings present another dimension to be considered when assessing urban water quality, leading to direct benefits to the management and use of urban waters.Integr Environ Assess Manag 2022;18:1260-1271.© 2021 SETAC.

Accurate analysis of spatiotemporal variations in algal biomass and their underlying causes is crucial for controlling algal blooms and enhancing aquatic ecological quality. The present study, spanning 2011 to 2020, was conducted across 10 sites in Lake Dianchi, where peak algal biomass levels occur from May to September, with higher concentrations in the lake’s northern zones compared to other areas. Employing Spearman’s correlation analysis, generalized additive models (GAMs), and random forest (RF) techniques, the relationships between algal biomass and water quality indicators were investigated. Spearman’s correlation analysis revealed a positive relationship between chlorophyll a (Chla) and total phosphorus (TP) across various spatial scales. RF analysis identified TP as the most influential factor on a lake-wide scale; while in localized RF models, organic pollution-related indicators (COD, CODMn, and BOD5) and TP consistently emerged as the primary predictors of Chla at most sites. GAM results indicated spatially variable and nonlinear responses of algal biomass to predictors, reinforcing TP’s significance lake-wide and at many localized scales. This comprehensive analytical approach provides valuable insights into the role of water quality factors and nonlinear dynamics, thereby advancing our understanding of the relationships between algal biomass and environmental conditions. These findings are pivotal for the development of scientifically informed strategies for lake management and conservation.

This study addresses the compound risks of drought and water quality deterioration faced by urban water supply systems under climate change. A dynamic allocation model integrating water quantity and quality indicators was developed to enhance adaptive management. The model incorporates an expanded water deficit index (WDI) and a Water Quality Index (WQI) within a fuzzy logic-based decision framework. Through a quality-weighting adjustment mechanism, the model dynamically reallocates surface water, groundwater, reclaimed water (RW), and interregional support water according to real-time hydrological and quality conditions. Using Taichung City, Taiwan, as a case study, results show that the proposed model improves overall supply resilience by 9.6% while maintaining system stability and safeguarding health under variable drought and water quality conditions. The findings demonstrate that integrating dual indicators within a fuzzy inference system provides a practical and scalable approach for adaptive, health-conscious, and climate-resilient urban water governance.

Water quality monitoring is essential to understanding the complex dynamics of water ecosystems, the impact of human infrastructure on them and to ensure the safe use of water resources for drinking, recreation and transport. High frequency in-situ monitoring systems are being increasingly employed in water quality monitoring schemes due to their much finer temporal measurement scales possible and reduced cost associated with manual sampling, manpower and time needed to process results compared to traditional grab-sampling. Modelling water quality data at higher frequency reduces uncertainty and allows for the capture of transient events, although due to potential constraints of data storage, inducement of noise, and power conservation it is worthwhile not using an excessively high sampling frequency. In this study, high frequency data recorded in Bristol's Floating Harbour as part of the local UKRIC Urban Observatory activities is presented to analyse events not captured by the current manual sampling and laboratory analysis scheme. The frequency components of the time-series are analysed to work towards understanding the necessary sampling frequency of temperature, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), turbidity and conductivity as indicators of water quality. This study is the first of its kind to explore a statistical approach for determining the optimum sampling frequency for different water quality parameters using a high frequency dataset. Furthermore, it provides practical tools to understand how different sampling frequencies are representative of the water quality changes.

As the climate and the external environment have changed, the environmental factors of the Lijiang River Basin (LRB) have changed, posing new threats to the environmental quality, ecosystem balance, and management and protection of the water environment of the Lijiang River. Water quality indicators and environmental factors vary spatially along the Lijiang River, which runs through urban areas, farmland, and karst areas. However, research on the response of water quality to water environmental factors is still lacking. Within this context, this study considered statistical methods and hydrological, meteorological, and water quality data of the middle and lower reaches of the Lijiang River from 2012 to 2018, expounded on the temporal and spatial change characteristics and evolution trends of water quality indicators; we analyzed the correlation between water quality indicators and environmental factors; we quantitatively assessed the sensitivity and contribution rate of water quality indicators to environmental factors. The results demonstrated that rainfall feedback on the river streamflow was lagging, and upstream precipitation often affected downstream streamflow. The water quality in the upper reaches of Guilin has improved year by year, and pollution levels have increased slightly when flowing through the urban area of Guilin. In spite of this, it still falls within the range of self-purification. River characteristics heavily influence the impact of environmental factors on water quality indicators; in contrast, the effects of different locations along the same river are more similar. Four water quality indicators are negatively correlated with water temperature, pH, and dissolved oxygen (DO). The sensitivities of ammonia nitrogen (NH4-N) and chemical oxygen demand (CODMn) to streamflow increase with the flow direction. The contribution rates of DO-to-total phosphorus (TP) and pH-to-TP are over −6%. Water temperature is the major contributing factor in the Lijiang River, while DO has a higher contribution in tributaries. The external sources affect the concentration of various water quality indicators and the sensitivity of water quality indicators to the external environment. There should be a series of measures implemented to reduce pollution, such as using oxygenation or chemical means to increase pH in Dahe and Yangshuo to control water pollutants. Tourism and particular karst topography make LRB’s calculations unique, but the research method can be applied to other watersheds as well.

In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH4+: 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.

Surface water is an important source for drinking water supply in Mahanadi Basin, Odisha. The research was done to evaluate the water quality, that serves as the source of domestic water supply to many cities. Samples of water were taken from nineteen important sampling areas for a period of 2010-2023 and twenty water quality parameters were examined to determine the WQI, followed by Multi-Criteria Decision-Making (MCDM) evaluation. Employing the Weighted Arithmetic (WA) Water Quality Index (WQI) and Stepwise Weight Assessment Ratio Analysis (SWARA) WQI, this study finds areas where cumulative variables, such as sewage discharge, a falling water table, dilution, and surface runoff, that tends to cause water quality variations in a water body, over a given monitoring period, have had the greatest impact. The WA WQI and SWARA WQI in the study area ranges from 23.78 to 96.09 and 14.6 to 1065.2, respectively. Also, the river water ranged from excellent to very poor, encompassing excellent for approximately 15.8%, good for 68.4%, poor for 10.5% and very poor for 5.3% in case of WA WQI. While the general water quality, as per SWARA-WQI, it varied from excellent to extremely poor, comprising 84.21% excellent, 10.53% poor and 5.26% for extremely poor category. The overall WQI in the study area indicates that the surface water is safe and potable except few localized pockets in SP-(8), (9) and (19) blocks. The cause could be attributed to anthropogenic sources such as domestic sewage and agricultural runoff altered a few parameters– e.g., TKN and TC. Based on geostatistical results, Gaussian model produce a more accurate assessment as per nugget/sill ratio, ASE and RMSE. To delineate the feasible regions for drinking practices, MCDM models such as Compromise Programming (CP), Ordered Weighted Averaging (OWA), and Combined Compromise Solution (CoCoSo), were adopted. Finally, the results demonstrated that WQI generated using both indexing strategies matched the outcomes of MCDM models. To sum up, it is advantageous and gives a clear image of water quality to combine physicochemical properties, WQIs, MCDM, and GIS technologies to evaluate surface water suitability for drinking and their controlling variables.

The severe contamination of groundwater supplies in rural areas is a global problem that requires strict environmental measures. Related to this, one of the most important challenges at present is the elimination of local sources of pollution. Therefore, this research examined the local water quality changes following the construction of the sewerage network, under the framework of long-term monitoring (2011–2022) in Báránd, Hungary, using water quality indices and GIS (Geographic Information System) techniques. In order to understand the purification processes and spatial and temporal changes, three periods were determined: the pre-sewerage period (2011–2014), the transitional period (2015–2018), and the post-sewerage period (2019–2022). Forty monitoring wells were included in the study, ensuring complete coverage of the municipality. The results revealed a high level of pollution in the area in the pre-sewerage period. Based on the calculated indices, an average of 80% of the wells were ranked in categories 4–5, indicating poor water quality, while less than 8% were classified in categories 1–2, indicating good water quality. No significant purification process was detected in the transitional period. However, marked changes were observed in the post-sewerage period as a result of the elimination of local sources of pollution. In the post-sewerage period, the number of monitoring wells ranked as excellent and good increased significantly. Additionally, the number of wells assigned to category 5 decreased markedly, compared to the reference period. The significant difference between the three periods was confirmed by the Wilcoxon test as well (p &lt; 0.05). Based on interpolated maps, it was found that, in the post-sewerage period, an increasing section of the settlement had good or excellent water quality. In addition to an assessment of long-term tendencies, the annual fluctuations in the water quality of the wells were also examined. This showed that the purification processes do not occur in a linear pattern but are influenced by various factors (e.g., precipitation). Our results highlight the importance of protecting and improving groundwater resources in municipal areas and the relevance of long-term monitoring of water adequate management policy.

Water pollution assessment remains a complex and critical task. To facilitate comprehensive evaluation, the Water Quality Index (WQI) serves as a reliable metric to quantify overall water quality in terms of type and extent. In this study, the WAWQI method was employed to calculate the WQI based on key parameters including pH, DO, COD, BOD, TS, TSS, TDS, turbidity, and hardness at the Omkareshwar Sampling Station, Pune district, Maharashtra, India, over a 4-year period (2019-2022). The calculated WQI values ranged between 55 and 57, classifying the river water quality as “poor”. To explore interrelationships among water quality parameters and their predictive utility, Pearson correlation and Multiple Linear Regression (MLR) analyses were conducted. A strong correlation was observed among the studied parameters, indicating potential for modelbased water quality assessment. The Regression analysis for the year 2019 yielded four MLR models: MLR1 (R² = 0.9799), MLR2 (R² = 0.9828), MLR3 (R² = 0.9837), and MLR4 (R² = 0.9953). These models were subsequently applied to estimate WQI for the year 2022. The predictive model explained 74% of the variance in WQI (R² = 0.74, p &lt; 0.05), confirming the applicability of historical water quality data in forecasting future trends. The study highlights the utility of correlation and regression approaches for water quality monitoring and supports the development of data-driven WQI models for effective environmental management. Major Findings: The study assessed water quality of the Mutha River (2019-2022) using the WAWQI method, with WQI values (55-57) indicating poor water quality. Significant correlations among parameters were identified, and multiple linear regression models (R² up to 0.9953) were developed to predict WQI, explaining 74% of the variance for 2022.

The Brahmani River, one of Odisha’s major freshwater sources and a vital habitat for native fish species, is facing significant water quality challenges. A study on hydro-chemical characterization of surface water and its suitability for drinking and irrigation purpose was carried out in and Rourkela, an industrial city of Odisha. Over the course of three years (2022 –2025), a total of 12 surface water samples were collected during the pre-monsoon season. This study presents a comprehensive assessment of the river’s water quality using multiple Water Quality Indices (WQI), including the British Columbia (BC) WQI, Canadian (C) WQI, Malaysian (M) WQI, Oregon (O) WQI, and Assigned (A) WQI. The pH levels varied from 5.33 to 7.06. This means that the water sample is slightly acidic to alkaline, making it suitable for various types of aquatic life. Analyses across pre-monsoon season reveal that key parameters such as conductivity, hardness, alkalinity, total dissolved solids, Pb2+, Cu2+, and Zn2+, consistently exceed national standards, resulting in water quality ratings ranging from “Poor” to “Very Poor.” The deterioration is more pronounced during the tested season, largely due to increased industrial discharge, urban runoff, and lower river flow. Surface water samples in the study area have BCWQI varying from 2 – 95, signifying 20% of water samples as suitable for drinking purpose. Computed CWQI score is estimated as: 27 – 98. Based on the CWQI classification, 40% of the tested specimens contributes fair – poor water quality. The calculated AWQI reading spanned between 36 to 345, indicating 20% of samples are excellent to good water, demonstrating suitable for use for human consumption. MWQI underscored the complex interactions among key pollutants, records a value between 17 and 90, indicating that 60% of samples contribute safe drinking standards. Notably, the OWQI value reports to be a range between 34 to 98. A substantial 20% of samples were classified as exceptional, 20% as good, 20% as fair, while 30% and 10% were grouped under poor and very poor water class. This comparison with case studies from around the world reveals a number of important trends: Agricultural runoff remains a key global source of pollution harming surface water quality; Industrial activities significantly exacerbate pollution, especially in rapidly industrializing nations; Proximity to water bodies is consistently a crucial factor in non-point source pollution impact on surface water; and Urbanization is emerging as an important contributor to non-point source pollution, particularly in developing regions. Hence, these ultimate outcomes align with previous studies on other Odisha rivers, underscoring widespread water quality degradation linked to anthropogenic activities. The results call for urgent action through enhanced pollution control measures, stricter regulatory enforcement, and sustainable management practices to safeguard the Brahmani River’s ecological health and the communities depending on it . Aligned with sustainable development goal (SDG) – 6, 11, 12, 13, and 15, these findings construct a solid foundation for well-informed regional policy making and water resource management that aims to reduce pollution from non-point sources and support sustainable surface water quality.