Changes In Water Quality Research Articles

Trace metals induce microbial risk and antimicrobial resistance in biofilm in drinking water

This study investigated the changes in water quality and microbial risks resulting from trace metal pollutants in stagnant drinking water conditions using a 168-h experimental simulation and a metagenomic approach. The results showed that Fe(III) increased the water turbidity. Stagnation also caused significant biofilm growth, which was increased by trace metal pollutants, resulting in a higher production of extracellular polymeric substances (EPS). Adaptive mechanisms of bacterial communities dominated by Pseudomonadota in response to trace metal pollutant stress were discovered. Pathogenic bacteria, particularly Salmonella enterica and Pseudomonas aeruginosa, were found in stagnant drinking water, potentially exacerbated by Al(III). The overall exposure risk of antibiotic resistance genes (ARGs) increased, whereas Fe(III) enhanced the co-occurrence of ARGs and pathogens, potentially leading to serious hidden microbial risks. This study reveals imperceptible microbial risks posed by trace metal pollutants in stagnant drinking water, providing scientific warning and advice for drinking water safety.

Preliminary analysis of water quality in the urban stretch of Paragominas stream, Pará, Brazil

The growth of the urban area of Paragominas is causing problems for water resources. This was observed due to the lack of data on the water quality of the Paragominas stream, in the urban stretch. This gap needs to be filled, which is why this research was conducted on this stream and this stretch. The aim was to verify the state of water quality using six parameters (five physical: temperature, electrical conductivity, total dissolved solids, turbidity and hydrogen potential; one chemical, dissolved oxygen) in five areas (A1, A2, A3, A4, A5), in the three sections (SI - right bank; SII - central channel; SIII - left bank). The methodology used was investigative with a qualitative approach. Primary data was obtained using electrometric instruments (multi-parameter probe, turbidimeter and thermometer with a probe). The data obtained and analyzed indicated that the water temperature in the five areas had an average value above the annual forecast (± 26 ºC/year) for the municipality (29.0±3.3); electrical conductivity had the highest average value in A3 (390±41.0); TDS, in A1, had the highest average (256±13.9); turbidity, was more pronounced in A2 (24.1±15.1); DO, the lowest value occurred in A5 (0.3±0.6). After applying for the PCA, it was identified that, of the six parameters used, the ones that may be contributing most to the change in water quality are EC and TDS. Based on these observations and the values obtained, the urban stretch of the Paragominas stream is at risk of significant changes leading to pollution and perhaps contamination.

Investigation of impact of split treatment on finished water quality for the Ames water treatment plant.

In response to limited carbon dioxide availability and increasing costs during the COVID-19 pandemic, an investigation into split treatment was performed. A 2020 pilot study showed that a 5% raw water bypass and target caustic alkalinity range of 26-36 mg/L as CaCO3 resulted in 25% and 7% carbon dioxide and lime savings, respectively. Considering this promising outcome, the potential for scaling and corrosion within the plant was evaluated during a subsequent split treatment trial in this study. Mild steel scale coupons were assessed for scaling and corrosion at four different treatment conditions. These results found that a 5% raw water bypass is predicted to have a minimal impact of scaling and corrosion on the water plant infrastructure when compared to full lime softening. Results are significant since split treatment can save over $150,000 annually with no apparent change in water quality, improving the plant's resilience and sustainability. PRACTITIONER POINTS: A 5% raw water bypass produced scale and corrosion similar to full lime softening. Localized build-up results in surface corrosion. Relocation of SHMP did not show appreciable sequestration of hardness ions. Adjusting dosage and/or using a polyphosphate blend is recommended. A 5% raw water bypass can be implemented at a lime softening facility to reduce chemical usage and cost. A similar procedure can be conducted to analyze the impacts of a larger percent bypass for additional chemical savings.

An Integrated Statistical, Geostatistical and Hydrogeological Approach for Assessing and Modelling Groundwater Salinity and Quality in Nile Delta Aquifer

The phenomenon of seawater intrusion is becoming increasingly problematic, particularly in low-lying coastal regions and areas that rely heavily on aquifers for their freshwater supply. It is, therefore, vital to address the causes and consequences of this phenomenon in order to ensure the security of water resources and the sustainable use of water. The objective of this paper was twofold: firstly, to delineate zones with different salinization levels over time; secondly, to investigate the factors controlling seawater intrusion of the Nile Delta aquifer. Aquifer data were collected in Sharkia governorate, Egypt, over three historical periods of years: 1996, 2007, and 2018. The dataset used to create the linear model of coregionalization consisted of hydrogeological (water level), hydrodynamic (pH, EC, Na, Mg, K, Ca, HCO3, SO4), and auxiliary (distances from salt and freshwater sources) variables. Cokriging was applied to produce spatial thematic maps of the studied variables for the three years of the survey. In addition, factorial cokriging was applied to understand the processes beyond the change in the aquifer water quality and map the zones with similar characteristics. Results of mapping the first factor at long range over the three years indicated that there was an increase in seawater intrusion, especially in the northeastern part of the study area. The main cause of aquifer salinization over time was the depletion of the groundwater resource due to overexploitation.

Research on the Integrated Analysis of Water Quality and Spatial‐Temporal Changes in Qionghai Lake

ABSTRACTThis paper used data from 2019, 2020, and 2021 to assess the water quality of Qionghai Lake using the traditional and improved Nemerow pollution index methods. To analyze the pattern of change in water quality between different Qionghai monitoring sections, a spatial‐temporal change rate calculation was conducted. According to the traditional Nemerow pollution index method, assessment results for 2019, 2020, and 2021 show that Qionghai as a whole, along with the Qinglong Temple water quality monitoring section, the Qionghai Hotel, and the lake center, all had Class I, superb water quality. However, Qionghai's water level was Class I in 2019 and 2021, according to the results of the improved Nemerow pollution index method, while the water quality assessment for 2020 showed that the water quality was Class II, with good but inconsistent water quality across the 3 years. From 2019 to 2020, the overall lake quality and the water quality of the lake center, Qionghai Hotel, and Qinglong Temple section have all experienced a significant decline of more than 20%. In 2021, there will be more than 20% substantial improvement in water quality. Qionghai Hotel experienced a slight decline of 10% to 20% in 2019 and a sharp deterioration of more than 20% in 2020. There was no significant difference in water quality changes between Qionghai Hotel and the Qinglongsi section between 2019 and 2020, and the change range was less than 10%.

Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds

Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies post-wildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R2 of 0.98 and root mean square error of 0.88 °C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 °C by 2050. RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predicting post-wildfire impacts and informing climate-resilient water management strategies.

Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review

Aquatic ecosystems play a crucial role in sustaining life and supporting key green and blue economic sectors globally. However, the growing population and increasing anthropogenic pressures are significantly degrading terrestrial water resources, threatening their ability to provide essential socioeconomic services. To safeguard these ecosystems and their benefits, it is critical to continuously monitor changes in water quality. Remote sensing technologies, which offer high-resolution spatial and temporal data over large geographic areas, including surface water bodies, have become indispensable for these monitoring efforts. They enable the observation of various physical, chemical, and biological water quality indicators, which are essential for assessing ecosystem health. Machine learning algorithms are well suited to handle the complex and often non-linear relationships between remote sensing data and water quality parameters. By integrating remote sensing with machine learning techniques, it is possible to develop predictive models that enhance the accuracy and efficiency of water quality assessments. These models can identify and predict trends in water quality, supporting timely interventions to protect aquatic ecosystems. This paper provides a thorough review of the major remote sensing techniques for estimating water quality indicators (e.g., chlorophyll-a, turbidity, temperature, total nitrogen and total phosphorous, dissolved organic, total suspended solids, dissolved oxygen, and hydrogen power). It examines how machine learning can improve water quality assessments. Additionally, it identifies key research gaps in current methodologies and suggests future directions to address challenges in water quality monitoring, aiming to improve the precision and scope of these critical efforts.

Response rate of submerged macrophyte chlorophyll content under changing light conditions

Abstract Lake browning, i.e., a gradual and long-term increase in the water colour, is a recognized and topical water quality problem that has received much less attention in water protection than eutrophication. Thus, new bioindicators that respond to environmental stress are needed to monitor lake browning. Recent study suggests that the chlorophyll content of macrophytes is a promising indicator for assessing lake browning, yet little is known about how quickly the changes in the total chlorophyll content of aquatic plants occur. For instance, it is not known whether the plants adjust their chlorophyll content to the daily changes in light availability or to the longer-term light environment in the lake. Therefore, the response time of macrophytes to changes in light availability needs to be clarified to assess their suitability for detecting changes in water quality. The objectives of this experimental study were to investigate the chlorophyll content of Egeria densa and their response time to changes in light availability. No significant change in total chlorophyll content was observed in E.densa, but our study showed a change in macrophyte chlorophyll a:b ratio within 5–7 days of light intensity alternation, suggesting the chlorophyll ratio can be a more promising indicator for the light conditions varying on the daily scale than the snapshot weather monitoring. Thus, our study corroborates the idea of using macrophytes as bioindicators of browning.

Microbial community structure and water quality performance in local scrubber reclaim system for water reclamation of the semiconductor industry: A case study of a semiconductor plant in Beijing.

The local scrubber reclaim (LSR) system plays a critical role in water reclamation and in reducing environmental pollution emissions in semiconductor factories. This study monitored the changes in water quality and assessed the key stages of pollutant removal, with a primary focus on evaluating microbial growth and the shifts in microbial community structure and function in the LSR system. The results showed that activated carbon filtration (ACF) effectively removed total organic carbon (TOC) with a removal rate of 59.35%, while ion exchange (IEX) was essential for reducing conductivity, with a removal rate of 87.33%. Furthermore, severe bacterial growth was observed (more than 1000CFU/ml) in the system. Bacteria numbers in the MMF and ACF stages grew dramatically, at least four times higher than that in the influent tank. After chlorination in the storage tank, microbial numbers sharply dropped, yet microbial diversity increased. The dominant microbial group in the LSR system was Patescibacteria (average relative abundance was 32.37%), considered part of the "microbial dark matter" and also known as Candidate Phyla Radiation (CPR). Following the effluent from the storage tank, the biofilm-forming potential of bacteria significantly increased (relative abundance rose from 7.19% to 15.20%), along with a varying increase in the abundance of genes related to metabolism. Measures should be implemented to prevent pipeline blockage and improve water reclamation efficiency.

Comprehensive Mitigation Plan for Environmental Impacts

Environmental Impact Assessment (EIA) is a systematic process designed to evaluate the potential environmental effects of proposed projects or developments before they are implemented. Its primary objective is to ensure sustainable development by identifying, predicting, and mitigating adverse environmental impacts while enhancing positive ones. EIA involves multiple stages, including screening, scoping, impact analysis, public consultation, mitigation planning, and decision-making. The process emphasizes public participation, interdisciplinary collaboration, and adherence to regulatory frameworks. Key areas of assessment include air and water quality, biodiversity, land use, socioeconomic impacts, and climate change implications. By integrating environmental considerations into project planning, EIA supports informed decision-making, reduces environmental harm, and fosters long-term ecological balance. It serves as a critical tool for governments, developers, and stakeholders to achieve environmental sustainability while addressing economic and social objectives.

Using the Forel-Ule index (FUI) to track the water quality of subsidence water bodies across the life cycle of coal mining in eastern China.

The water quality and associated ecological risks in subsidence water bodies formed by underground coal mining are an increasing global concern. However, long-term water quality changes in these subsidence water bodies, especially across different spatial regions, remain poorly understood. This paper, by mapping the Forel-Ule index (FUI) a key indicator of water color, using Landsat datasets to reveal the dynamic evolution of water quality in 402 subsidence water bodies in the Huang-Huai-Hai Plain of eastern China from 1990 to 2020, covering their life cycle from formation to extinction. We identified three types of subsidence water bodies, including growing (14.4%), stable (35.1%), and shrinking (50.5%), almost all of which were found to exhibit eutrophic conditions. The findings revealed a blue-shift trend, indicative of improved water quality, was observed in nearly half (45.3%) of the water bodies. During mining, water quality was generally poor with higher average FUI values, but gradually improved at an average rate of -0.09yr⁻1. FUI values experienced a brief period of stability before deteriorating post-mining, with an average rate of 0.05yr⁻1. Our study provides valuable insights into the governance of subsidence water bodies in coal mining areas by revealing large-scale, long-term trends in water quality evolution.

A repercussion of COVID-19 lockdown on water quality along the east coast of India.

The COVID-19 pandemic and subsequent lockdown measures significantly impacted various sectors, including coastal environments. While restrictions led to temporary improvements in air quality, their effects on coastal waters remained understudy. This research conducted four cruises along the east coast of India during pre- and post-COVID-19 lockdown to assess the water quality changes. Results show a significant increase in Chl-a (31.8%), DO (28.1%), and SDD (7.7%), while reductions in NO3 (34.7%), PO4 (51.7%), SiO4 (16.2%), TSM (25.4%), TC (72.3%), and FC (83.3%) were observed. Multivariate analysis identified land-based pollution as the primary source of pollution in coastal waters. Overall, the findings suggest improved coastal water quality during the lockdown. However, for the sustainability of coastal waters, it is proposed that raw sewage, wastewater, and atmospheric fluxes affecting coastal water quality must be regulated.

Seasonal instability of phytoplankton community in extreme arid lake basin during interval period of large-scale ecological water transport.

Large-scale water transport helps solve the imbalance in water resources. Studies on ecological benefits after long-term EWT mainly focus on vegetation restoration and increase in water surface. However, the maintenance of aquatic communities in the EWT context is a major challenge. This study studied the seasonal dynamics of phytoplankton communities during the interval period of 22nd and 23rd EWT in a newborn lake generated by long-term EWT in an extremely arid zone. The 22nd EWT was carried out from August 2021 to November 2021, and the 23rd EWT was implemented from July to September 2022. Our research indicates that during the interval period, water temperature (WT), pH, dissolved oxygen (DO), suspended solids (SS), turbidity, total nitrogen (TN), NO3-N, total phosphorus (TP), chemical oxygen demand (CODcr), SO42- showed seasonal dynamic changes. Meanwhile, the phytoplankton community also exhibits seasonal variations, including the succession of dominant phyla and dominant species, the decrease in density and α diversity, as well as the increased instability of functional groups and ecological networks. These phenomena are more pronounced in summer when the lake water evaporates and the water quality changes, suggesting that the phytoplankton community shows attenuation along with the changes of environmental factors during the interval period. Based on this, our study suggested that the coupling relationship between water quantity, water quality change and aquatic community succession should be considered for the precise control of EWT projects aiming at improving ecological benefits. Regular phytoplankton monitoring is recommended to track the evolution of aquatic ecosystems after long-term EWT.

Control mechanism of Escherichia coli invasion by micro-nano bubbles in drinking water distribution system.

Sudden biological contamination in Drinking Water Distribution System (DWDS) significantly threatens the safety of drinking water, with E. coli invasions being particularly hazardous to human health. Traditional disinfection methods (i.e., chlorine, ultraviolet and ozone) provide partial microbial reduction. Micro-nano bubbles (MNBs) offer a promising alternative due to easy preparation, environmental friendliness, and generating ·OH in situ. This study explored the control mechanism of MNBs using different gas sources (i.e., nitrogen, air, oxygen, and ozone) on E. coli invasion in drinking water of the secondary water supply tanks. MNB characteristics, water quality changes, bacterial concentration, and microbial communities were evaluated. Results indicated that E. coli gradually became the dominant bacterium by promoting species interaction and influencing the process of microbial community construction, leading to a 6.25% increase in bacterial counts in water. MNBs generated via a dissolved gas release method exhibited particle sizes ranging from 500 to 800 nm and Zeta of -0.6 to -3.1 mV, and the bubble collapse effect generated a large amount of ·OH (0.11 ∼ 0.40mmol/L), which reduced bacterial abundance by 66.53% and microbial community richness, as revealed by decreases in the Chao (10.53%) and ACE (3.75%) indexes. The oxidative stress induced by ·OH inhibited protein transcription and energy production, which damaged DNA repair mechanisms. Thus, the relative abundance of Gammaproteobacteria, including E. coli as the dominant strain, decreased by 47.6%, leading to a balanced microbial community. Additionally, MNBs showed a complete reduction of bacteria, such as Caldisericia and Fusobacteria, thereby improving the drinking water safety and biological stability. This study highlights the potential of MNBs to address sudden exogenous biological pollution in DWDS, providing critical theoretical support to ensure the safety of drinking water quality.

Macrozoobenthos of the rivers of North-Eastern Azerbaijan

Between 2020 and 2023, an in-depth analysis was conducted on the species composition of the macrozoobenthos in the Yalama River, including its tributaries Telchay, Mukhtadyrchay, Velmirchay, and Khuraychay, as well as other significant rivers in northeastern Azerbaijan. During this period, a total of 53 species of benthic organisms, representing 14 different taxonomic groups, were identified across the rivers studied. These findings provide important insights into the biodiversity and ecological health of the region's freshwater ecosystems. Among the various groups recorded, mollusks, dragonflies, crustaceans, and chironomid larvae were particularly prominent, both in terms of species richness and their ecological significance within the aquatic habitats. These groups are crucial for understanding the dynamics of the river systems, as they play key roles in nutrient cycling, sediment stability, and the food web. The diversity of these species also reflects the environmental conditions and the unique characteristics of the rivers in this region, which may be influenced by factors such as water quality, habitat variation, and seasonal changes.

Temporal changes in water quality in Leh Ladakh region: Impact of urbanization

Water is a valuable and limited resource in semi-arid regions like Ladakh. Effective management and conservation of water are crucial to prevent negative consequences on the area's quality of life. Since becoming a Union territory, Leh, a district of Ladakh, has undergone rapid urbanization due to its administrative status, air service facilities, tourism, and increasing population. However, this urbanization and tourism boom have resulted in a higher demand for water and a decline in its quality. Glacial-fed water is the primary source for drinking and agriculture in Ladakh. As Ladakh has become a popular tourist destination, the distribution and quality of water have been negatively affected. Construction of hotels and guest houses on agricultural lands, could further harm Ladakh's fragile ecological environment. Due to the challenging terrain and harsh conditions, there has been limited research on water quality in the region andare confined to the Leh district only. Despite lack of comprehensive information, this review aims to address three important questions: the hydrochemistry of water resources, the impact of urbanization on water quality, and the existing research gap in hydrochemistry in significant areas and water resources. The objective is to establish fundamental data for future research and contribute to a better understanding of water resources in the region.

Assessing Seasonal Variations in Reservoir Water Quality: Implications for Eutrophication and Pollution Management

Surface water is a strategic freshwater reserve that meets the needs of households, agriculture, livestock, industry, and research. Surface water quality is affected by anthropogenic activities and seasonal variations, which can pose ecological risks. This study aimed to assess the water quality of the Darma Reservoir, the status of water quality and trophic levels, and trends in water quality changes in the rainy and dry seasons. The study was conducted for one year, from October 2023 to September 2024, covering the rainy and dry seasons. Sampling was carried out at eight stations spread across three zones of the Darma Reservoir, namely the inlet zone, utilization zone, and outlet zone. Water quality parameters were tested using PCA, the water sample measurements were compared with water quality standards (PP/22/2021), and the Regulation of the Minister of State for the Environment number 28 of 2009 was analyzed using the STORET index. The results of the study showed differences in water quality characteristics between seasons, where the concentration of Total Nitrogen (TN) showed an increase in the rainy season, while the concentration of Total Phosphate (TP) was higher in the dry season.

Groundwater well fields in ice-margin valley aquifers - is it easy to protect them, or not? An overview of hydrogeological and legal aspects of determining wellhead protection zones

This paper discusses principles for delineating wellhead protection zones (WHPZ) of groundwater well fields in ice-margin valleys. A distinctive feature of such well fields is that, apart from the often geogenically contaminated water of ice-margin valleys, they are largely supplied with high-quality water from intertill aquifers of neighbouring uplands. However, much of this inflow can be intercepted by wells for agriculture that are increasingly being constructed in the capture zones of existing municipal well fields, thus posing a threat to the quality of water for the public. This problem has been investigated using the example of a municipal well in Wroniawy (Poland) by analysing changes in the recharge components of this well field with a groundwater flow model. The results indicate that the commissioning of agricultural abstractions in the capture zone of this well field reduces inflow from intertill aquifers (8.5 per cent) and precipitation recharge (3.3 per cent), following a change in the extent of the capture zone. The loss of these qualitative recharge components is substituted by an increase in poor-quality water, i.e., surface water (7.3 per cent) and geogenically contaminated water from the ice-margin valley centre (3.8 per cent). Protecting well fields in such locations from adverse water quality changes requires the implementation of quantitative shielding of best-quality water, calling for WHPZ to cover the entire capture zone regardless of water flow timing, which is not provided for in Polish legislation. Costs and constraints of implementing such a WHPZ can be reduced by dividing it into sectors that differ in the scope of limitations, with the only quantitative protection applied to the outermost, medium- and low-vulnerable parts of the capture zone.

Assessing Changes in China's Pond Water Quality From 1989 to 2020: Implications for Green Development in Aquaculture

ABSTRACTSince 1989, China has become the world's largest aquaculture producer through pond culture, significantly contributing to global food security but potentially posing environmental threats. However, little is known about the changes in the pond water quality at broad spatial and temporal scales, thus creating challenges in the sustainability of pond aquaculture. Herein, we explore changes in water quality in China's aquaculture ponds from 1989 to 2020 and assess the waterbody's health status to optimize sustainable management strategies. This study highlights pond water quality changes closely associated with China's aquaculture practices, development, and related policies. Seasonal and regional farming variations significantly impact water quality, with peak aquaculture seasons and Central China and North China associated with the lowest water quality scores. Pond water quality initially declined with the rise of intensive aquaculture but improved after 2012 with the adoption of ecological engineering measures, eco‐farming practices, and policies supporting green development. Under a scenario combining these efforts, the water quality score in 2035 is projected to increase by 57% of the 2020 level. These findings reveal that national ecological initiatives and advancements in green aquaculture techniques and public policies can significantly enhance the water quality of China's aquaculture ponds, offering valuable insights into environmental governance in global agriculture.

USE OF GEO-INFORMATION SYSTEMS FOR GROUNDWATER MONITORING

Abstract . The article is devoted to the study of the possibilities of using geographic information systems (GIS) for monitoring the state of groundwater, which is important for ensuring environmental safety and sustainable management of water resources. Groundwater is an important source of fresh water for the population, industry and agriculture, but it is vulnerable to various pollutions caused by both natural and anthropogenic factors. Therefore, the implementation of reliable monitoring systems that allow timely detection, assessment and forecasting of changes in water quality is necessary to prevent the deterioration of the ecological situation and prevent the spread of pollution. Geographic information systems (GIS ) offer significant opportunities for the collection and integration of large volumes of spatial data, including satellite images, remote sensing results, automated sensor data, and field observations. GIS allow creating multi-layered maps reflecting the ecological state of underground aquifers, modeling pollution processes and predicting their possible impact on nearby ecosystems and water sources. Thanks to the use of GIS, it is possible to combine data from different sources in a single interactive system, which simplifies analysis and provides the ability to quickly respond to any detected deviations. The article examines in detail the key methods used within GIS for groundwater monitoring, including satellite sounding to analyze changes in the earth's surface, spectral indices to assess the state of vegetation and soil salinity, and radar methods to determine soil electrical conductivity. Particular attention is paid to the application of three-dimensional modeling, which allows visualization of the spread of pollution in underground aquifers, which facilitates decision-making regarding the optimal location of monitoring stations and the design of protective structures. In combination with automated sensors recording water parameters in real time, GIS provides continuous monitoring of the physico-chemical indicators of groundwater and allows for prompt assessment of their condition. The article also analyzes the possibilities of using predictive modeling in GIS to assess the further spread of pollution and develop environmental risk management measures. It is described how mathematical models in GIS can help predict the impact of anthropogenic and natural factors on groundwater, which ensures the development of scientifically based solutions to reduce pollution and maintain water quality at a safe level. The implementation of GIS in groundwater monitoring is an important step towards increasing the efficiency of water resources management, ensuring their protection from pollution and preserving the ecological stability of the regions.