A case study of factors controlling water quality in two warm monomictic tropical reservoirs located in contrasting agricultural watersheds

Assessment of pollution in Alibeykoy Dam Lake (Istanbul, Türkiye) and its influent streams: Phytoplankton composition and heavy metal accumulation

In this study, the pollution levels of Alibeykoy Dam Lake were assessed by examining phytoplankton distribution, physicochemical parameters, nutrient content, and heavy metal concentrations. Alibeykoy Dam is a critical drinking water source for the Istanbul metropolitan area. Water samples were collected from the lake and six influent streams (Cebeci, Pirincci, Sidan, Bolluca, Gulgen, and Kocaman) in January, February, May, and September of 2021. A total of 36 taxa from seven divisions were identified, including Bacillariophyta (14), Charophyta (2), Chlorophyta (9), Cryptophyta (1), Cyanobacteria (3), Euglenozoa (4), and Miozoa (3). Diatoms were found to be the dominant group in terms of species richness and abundance. The dominant species recorded in each site were as follows: Microcystis aeruginosa in Alibeykoy Dam and Sidan Creek, Cyclotella ocellata in Cebeci and Gulgen Creeks, Scenedesmus sp. in Pirincci Creek, Navicula cryptocephala in Bolluca Creek, and Sphaerocystis planctonica in Kocaman Creek. Mesotrophic and eutrophic phytoplankton species suggested that the lake is nearing eutrophic conditions. This conclusion was further supported by high concentrations of heavy metals and nutrients detected in the water samples. This research is significant because it provides a comprehensive understanding of the ecological status of Alibeykoy Dam Lake, a critical resource for drinking water in Istanbul. The study highlights potential risks associated with eutrophication and heavy metal accumulation by identifying pollution levels and dominant species. These findings are vital for implementing effective water resource management strategies, ensuring the lake's sustainability, and protecting public health. PRACTITIONER POINTS: The pollution rate of Alibeykoy Dam Lake was determined. The presence of mesotrophic and eutrophic species of phytoplankton indicated the lake's trophic structure. The measurements were done to estimate the lake's heavy metal and nutrient contents.

The COP27 screened through the lens of global water security

Water security is an expression of resilience. In the recent past, scientists and public organizations have built considerable work around this concept launched in 2013 by the United Nations as "the capacity of a population to safeguard sustainable access to adequate quantities of acceptable quality water for sustaining livelihoods, human well-being, and socio-economic development, for ensuring protection against water-borne pollution and water-related disasters, and for preserving ecosystems in a climate of peace and political stability". In the 27th Conference of the Parties (COP27), held in Sharm El-Sheikh (Egypt) in last November, water security was considered a priority in the climate agenda, especially in the adaption and loss and damage axes. This discussion paper represents the authors' opinion about how the conference coped with water security and what challenges remain to attend. As discussion paper, it had the purpose to stimulate further discussion in a broader scientific forum.

Water quality analysis based on phytoplankton and metal indices: a case study in the Sauce Grande River Basin (Argentina)

The increasing landscape alterations due to anthropogenic activities is of global concern since it affects aquatic ecosystems, often resulting in compromise of the ecological integrity and the water quality. In this sense, the evaluation, monitoring, and prediction of the aquatic ecosystem quality becomes an important research subject. This study presents the first integrated water quality assessment of the Sauce Grande River Basin, in Argentina, based on the spatial distribution of the phytoplankton community, the physicochemical parameters, and the metal concentrations (Cd, Cu, Cr, Fe, Mn, Ni, Pb, and Zn) found in the particulate fraction. According to the trophic indices and the phytoplankton abundance, composition, and diversity, the water quality showed significant deterioration in the lower basin after the Sauce Grande lake. The trophic state index indicated that water was oligotrophic in over 75% of the sampling sites, increasing downstream, where two sites were characterized as mesotrophic, and one described as hypertrophic. The phytoplankton community was dominated by diatoms in zones with low anthropogenic impact and conductivity, whereas high densities of Euglenophyta, Chlorophyta, and Cyanobacteria were found in the middle-lower basin, associated with higher organic matter and eutrophication. The conductivity, turbidity, and most metal concentrations also increased towards the downstream area, even exceeding recommended levels for the metals Cu, Cr, Mn, and Pb in the middle and lower reaches of the basin (Cu: 3.5 µg L^-1; Cr: 2.4 µg L^-1; Pb: 1.2 µg L^-1; Mn 170 µg L^-1). This study generates a database for the water quality of the Sauce Grande River Basin and sets an example of how the water quality varies along a basin that crosses different topographic environments, land covers, and anthropogenic influences.

Autochthonous sources and drought conditions drive anomalous oxygen-consuming pollution increase in a sluice-controlled reservoir in eastern China

Freshwater reservoirs are an important type of inland waterbody. However, they can suffer from oxygen-consuming pollution, which can seriously threaten drinking water safety and negatively impact the health of aquatic ecosystems. Oxygen-consuming pollutants originate from both allochthonous and autochthonous sources, and have temporally and spatially heterogeneous drivers. Datanggang Reservoir, China, is located in a small agricultural watershed; it is controlled by multiple sluice gates. Anomalously high oxygen consumption indicators were observed in this reservoir in March 2021. Here, it was hypothesized that autochthonous sources were the primary drivers of oxygen-consuming pollution in the reservoir under drought conditions. Datasets of water quality, precipitation, primary productivity, and sediment were used to analyze water quality trends in the reservoir and inflow rivers, demonstrating the effects of allochthonous inputs and autochthonous pollution. No correlation was found between reservoir oxygen consumption indicators and allochthonous inputs; reservoir oxygen consumption indicators and chlorophyll-a concentration were significantly positively correlated (p < 0.05). Substantially lower precipitation and higher water temperature and pH (compared to historical levels) were also observed before the pollution event. Therefore, during this period the hydrological conditions, water temperature, pH, and other variables caused by short-term drought conditions may have facilitated phytoplankton growth in the reservoir. This contributed to a large increase in autochthonous oxygen-consuming pollutants, as reflected by the abnormally high indicators. Sediments contaminated with organic matter may also have been an important contributor. As the effects of environmental management and pollution control continue to emerge, exogenous pollutants imported from the land to reservoirs are currently effectively controlled. However, endogenous pollutants driven by a variety of factors, such as meteorology and hydrology, will likely become the main drivers of short-term changes in oxygen-consuming pollution in freshwater reservoirs in the foreseeable future.

Spatial indicator of priority areas for the implementation of agroforestry systems: An optimization strategy for agricultural landscapes restoration

The ecological functions restoration in agricultural areas is a major challenge on a landscape scale. In the specific case of active restoration through Agroforestry Systems (AFS), the absence of a specific direction hinders ecological restoration processes, especially in regions that prefer intensive agriculture. Thus, this study aims to develop a Spatial Indicator of Priority Areas to guide Agroforestry Systems implementation in agricultural landscapes. A spatial multicriteria decision analysis (MDCA) was carried out based on environmental factors: soil, geology and slope (which determine the natural vulnerability of the land) and anthropogenic factors: land use and land cover, forest fragments, potential land use capacity and legal protected areas in rural properties (which reflects human pressure and land use suitability). Subsequently, four priority levels were classified for agroforestry interventions: (1) Low priority; (2) Average priority; (3) High priority; (4) Extreme priority. A final map was made to identify priority areas for landscape recovery in 9 cities located at the mouth of the Mogi Guaçu River Hydrographic Basin, State of São Paulo, Brazil. Considering the natural vulnerability of the land and the multifunctional aspects of the landscape, the scenarios projection allowed a consensus for forest conservation and agricultural suitability perspectives. A final combination of the explored aspects culminated in the spatial indicator, which model foresees 22,300 ha available for urgent actions for restoration, reforestation and sustainable exploitation through agroforestry systems. We emphasize the challenges in reconciling the socioeconomic and ecological functions in the agroecosystem, however, the metric provides a more inclusive and assertive management strategy for natural resources and advances towards the goal of reforestation and implementation of payment for environmental services (PES) schemes.

Role of Mine Tailings in the Spatio-Temporal Distribution of Phosphorus in River Water: The Case of B1 Dam Break in Brumadinho

Human actions in the drainage network of hydrographic basins interfere with the functioning of ecosystems, causing negative impacts on the environment. Among these impacts, mass loads with a high concentration of phosphorus (P) have a significant potential for point and diffuse pollution of freshwater. The objective of this work was to model P spatially in the Paraopeba River basin, namely in the main water course and 67 sub-basins, and temporally in the years of 2019, 2020, and 2021, after the rupture of B1 tailings dam of Vale, SA company in Brumadinho (Minas Gerais Brazil). The distribution of total phosphorus concentrations (Pt) in relation to environmental attributes (terrain slope, soil class, and land use) and stream flow was assessed with the help of SWAT, the well-known Soil and Water Assessment Tool, coupled with box-plot and cluster analyses. The Pt were obtained from 33 sampling points monitored on a weekly basis. Mean values varied from 0.02 to 1.1 mg/L and maximum from 0.2 to 15.9 mg/L across the basin. The modeling results exposed an impact on the quality of Paraopeba River water in a stretch extending 8.8–155.3 km from the B1 dam, related with the rupture. In this sector, if the contribution from the rupture could be isolated from the other sources, the average Pt would be 0.1 mg/L. The highest Pt (15.9 mg/L) was directly proportional to the urban area of a sub-basin intersecting the limits of Betim town and Belo Horizonte Metropolitan Region. In general, urban sprawl as well as forest-agriculture and forest-mining conversions showed a close relationship with increased Pt, as did sub-basins with a predominance of argisols and an accentuated slope (>20%). There were various moments presenting Pt above legal thresholds (e.g., >0.15 mg/L), mainly in the rainy season.

Modelling of threats that affect Cyano-HABs in an eutrophicated reservoir: First phase towards water security and environmental governance in watersheds

Cyano-HABs are proliferating around the world due to anthropogenic nutrient enrichment of freshwater bodies. This study seeks to obtain a holistic vision over the various threats that affect the Cyano-HABs of Umia basin and especially of A Baxe reservoir (Galicia, NW Spain), through the method of Partial least squares path modelling (PLS-PM). The A Baxe reservoirs is a fundamental source of drinking water supply to surrounding dwellings. This study identifies and quantify the variables that increase contaminant concentration and decrease ecological integrity, as well as how this scenario evolved over various hydrologic years. In this regard, the PLS-PM equations will be robust and powerful tools to predict changes in eutrophication and ecological integrity, as response to measures implemented in the basin that can improve water quality. The dependent latent variables are "Eutrophication" (chlorophyl-a, Microcystis sp.) and "Ecological Integrity" (METI Bioindicator). The independent latent variables are "SWP", which represents surface water parameters (phosphorus, nitrogen and pH) and "Climatic Conditions" (temperature, precipitation). The PLS-PM results revealed that 51.0% of "Eutrophication" is predicted by the independent variables. The connections between latent variables are quantified through path coefficients (β). The "SWP" contributes by increasing "Eutrophication" (β = 0.235), the same occurring with the "Climatic Conditions" (β = -0.672). The variables "Eutrophication" (β = -0.217) and "SWP" (β = -0.483) lower the "Ecological Integrity". On the other hand, different trophic scenarios, adapted to the temperature increase predicted for the study area, were tested, and it was found that ecological integrity would improve by 46% if the oligotrophic state were reached. Therefore, it is recommended to prevent pollution by means of water control and governance plans, as well as corrective and preventive measures, which guarantee the water security of the river basins. Despite the complex mathematics behind the PLS-PM models, their user-friendly development and application through interactive graphical interfaces make them easily transposable to other eutrophic reservoirs, widening the readership of these studies focused on multiple-geosphere assessment of environmental impacts.

The Effect of Urban Land-Use Change on Runoff Water Quality: A Case Study in Hangzhou City

The main functions of this research are to guide the proportion of urban land that is used and the layout of the facilities on it, help understand the changes to surface runoff that are caused by land being used in urban development, and thus solve surface runoff pollution. Hangzhou City, China has been selected for the experiment, and the way in which its land is utilized as well as the grading of urban construction projects in the demonstration area are specifically analyzed. This study systematically distinguishes the definitions of impervious area based on the Sutherland equation and analyzes the impact of different impervious area subtypes on surface runoff water quality. Then, we compare the impact of impervious area subtypes with the impact of other land-use patterns on surface runoff water quality. This study shows the relationship between different land-use types and runoff water bodies: Land-use index can affect runoff water quality; Greening activities, impervious surface, and the water quality index are negatively correlated; the effective impervious area rate is positively correlated with the water quality index. The paper suggests that increasing the proportion of green spaces and permeable roads in build-up land reduces the effective impervious area (EIA) and thus controls land runoff pollution and improves runoff water quality.

Early warning of water quality degradation: A copula-based Bayesian network model for highly efficient water quality risk assessment

In the context of global climate change and increasingly severe environmental pollution, drinking water quality risk assessments to provide crucial early warnings have become essential routine work. At present, traditional water quality assessment methods are commonly used without considering the correlation among different indicators and the substantial uncertainty from multiple sources, which limit their applications. To address this issue, a copula-based Bayesian network (CBN) method was proposed in this study to concretely evaluate the water quality risk with multiple environmental risk indicators in a large drinking water reservoir in Tianjin city, China. Taking rainfall and water temperature (WT) as external environmental risk indicators and pH, ammonia nitrogen (NH₃-N), total nitrogen (TN), total phosphorus (TP), and permanganate index (COD_Mn) as internal environmental risk indicators, the CBN model was constructed to investigate the interaction between the indicators and water quality state and assess the contingent risk. Our results showed that TN and NH₃-N should be considered key risk indicators. Additionally, we performed forward and backward risk analyses to assess water quality risk during different seasons and determined the distributions of key indicators under different water quality risk grades. From a time perspective, the reservoir's water quality risk is much higher in winter and spring than in other seasons affected by winter snowfall. From a spatial perspective, the water quality risk is much higher at the reservoir's entrance than at other locations affected by water diversion. Furthermore, we found that the probability of water quality risk events may be relatively high when the TN concentration is 3.6 mg/L to 6.4 mg/L at the reservoir's entrance. The results reveal that the CBN method could be an invaluable decision-support tool for reservoir managers and scientists, which could provide an early warning of water quality degradation by only inputting monitoring data.

Assessment of Water Quality in A Tropical Reservoir in Mexico: Seasonal, Spatial and Multivariable Analysis

Agricultural activities are highly related to the reduction of the availability of water resources due to the consumption of freshwater for crop irrigation, the use of fertilizers and pesticides. In this study, the water quality of the Adolfo López Mateos (ALM) reservoir was evaluated. This is one of the most important reservoirs in Mexico since the water stored is used mainly for crop irrigation in the most productive agricultural region. A comprehensive evaluation of water quality was carried out by analyzing the behavior of 23 parameters at four sampling points in the period of 2012-2019. The analysis of the spatial behavior of the water quality parameters was studied by spatial distribution graphs using the Inverse Distance Weighting interpolation. Pearson correlation was performed to better describe the behavior of all water quality parameters. This analysis revealed that many of these parameters were significantly correlated. The Principal Components Analysis (PCA) was carried out and showed the importance of water quality parameters. Ten principal components were obtained, which explained almost 90% of the total variation of the data. Additionally, the comprehensive pollution index showed a slight water quality variation in the ALM reservoir. This study also demonstrated that the main source of contamination in this reservoir occurs near sampling point one. Finally, the results obtained indicated that a contamination risk in the waterbody and further severe ecosystem degradations may occur if appropriate management is not taken.

Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures

The inadequate management of soils and the absence of conservation practices favor the degradation of pastures and can trigger adverse environmental alterations and damage under the terms of Brazilian Federal Law no. 6.938/1981. Based on this premise, this study aimed to estimate soil losses caused by water erosion in pasture areas using the brightness index (BI) from the annual series of Landsat 8 images in different geological formations. A specifically prepared Google Earth Engine (GEE) script automatically extracted the BI from the images. The study occurred in the Environmental Protection Area (EPA) of Uberaba River basin (Minas Gerais, Brazil). To accomplish the goal, 180 digital 500-wide random buffers were selected from 3 geologic types (60 points per type), and then analyzed for zonal statistics of USLE (Universal Soil Loss Equation) soil loss and BI in a Geographic Information System. The regression models BI versus USLE soil loss allowed estimating BI soil losses over the pastures of EPA. The model fittings were remarkable. The validation of soil loss maps in the EPA occurred in pasture phytophysiognomies through the probing of penetration resistance in 37 randomly selected locations. The results were satisfactory, mostly those based on the BI. The BI losses increased for greater resistances. Amplified losses also occurred in regions exposed to environmental land use conflicts (actual uses that deviate from land capability or natural use). Overall, the BI approach proved efficient to accurately track soil losses and pasture degradation over large areas, with the advantage of standing on a single parameter easily accessed through remote sensed data. From an environmental standpoint, this is an important result, because the accurate diagnosis and prognosis of degraded pastures is paramount to implement mitigation measures following the "polluter pays principle", even more in Brazil where the areas occupied by degraded pastures are enormous.