Influence Water Quality Research Articles

Time series-based machine learning for forecasting multivariate water quality in full-scale drinking water treatment with various reagent dosages

Accurately predicting drinking water quality is critical for intelligent water supply management and for maintaining the stability and efficiency of water treatment processes. This study presents an optimized time series machine learning approach for accurately predicting multivariate drinking water quality, explicitly considering the time-dependent effects of reagent dosing. By leveraging data from a full-scale treatment plant, we constructed feature-engineered time series datasets incorporating influent water quality parameters, reagent dosages and effluent water characteristics. Seven predictive models, including both traditional machine learning (ML) and deep learning (DL) models were developed and rigorously evaluated against a naive mean baseline model. Our results demonstrate that traditional ML models, enhanced with time feature engineering, rivaled the performance of both widely used DL models and the naive mean baseline model. Specifically, an XGBoost model achieved superior prediction accuracy in dynamically forecasting four water quality characteristics at a 12-hour time lag step, outperforming the naive baseline model by 3-4% in terms of Mean Absolute Percentage Error (MAPE). This finding underscores the importance of incorporating a 12-hour interval to effectively capture the delayed impact of reagent dosing on water quality prediction. Furthermore, SHAP model interpretability analysis provided valuable insights into the XGBoost model's decision-making process, revealing its strong data-driven foundation aligned with established water treatment principles. This research highlights the significant potential of optimized machine learning techniques for enhancing water purification processes and enabling more informed, data-driven decision-making in the water supply industry.

Hydrogeochemical Processes in Basement Areas Using Principal Component in Burkina Faso (West African Sahel)

The basement aquifers in Burkina Faso are increasingly exposed to groundwater pollution, largely due to socio-economic activities and climatic fluctuations, particularly the reduction in rainfall. This pollution makes the management and understanding of these aquifers particularly complex. To elucidate the processes controlling this contamination, a methodological approach combining principal component analysis (PCA) and multivariate statistical techniques was adopted. The study analyzed sixteen physicochemical parameters from 58 water samples. The primary objective of this research is to assess groundwater quality and deepen the understanding of the key factors influencing the spatial variation of their chemical composition. The results obtained will contribute to better planning of preservation and sustainable management measures for water resources in Burkina Faso. The results show that three principal components explain 72% of the variance, identifying anthropogenic inputs, with two components affected by mineralization and one by pollution. The study reveals that the groundwater is aggressive and highly corrosive, with calcite saturation. Water-rock interactions appear to be the main mechanisms controlling the hydrochemistry of groundwater, with increasing concentrations of cations and anions as the water travels through percolation pathways. PCA also revealed that the residence time of the water and leaching due to human activities significantly influence water quality, primarily through mineralization processes. These results suggest that rock weathering, coupled with reduced rainfall, constitutes a major vulnerability for aquifer recharge.

Water Quality Changes in the Xingkai (Khanka) Lake, Northeast China, Driven by Climate Change and Human Activities: Insights from Published Data (1990–2020)

Water quality degradation and eutrophication of lakes are global ecological and environmental concerns, especially shallow lakes. This study collected hydrochemical data from 2935 samples of the Chinese part of Xingkai (Khanka) Lake, based on 40 published papers spanning the period from 2001 to 2023. Using the water quality index (WQI), improved geo-accumulation index (Igeo), and redundancy analysis (RDA), we analyzed the overall contamination characteristics of the water environment in Xingkai Lake. Additionally, we explored the impact of climate change and human activities on the lake’s water quality. The results showed that the annual WQI for Xingkai Lake ranged from 47.3 to 72, with a general downward trend, indicating improving water quality. Notably, the average WQI in May and total nitrogen (TN) content decreased significantly, signaling further improvement in water quality. The average concentration of TN in sediments was 1401.3 mg/kg, reflecting mild contamination. The Igeo values for the heavy metals Hg and Cr were greater than 1, indicating moderate contamination, while the Igeo values for Cd and Pb were between 0 and 1, which is in the range of uncontaminated to moderately contaminated. Land use and climate change (average annual temperature and annual precipitation) were key factors influencing water quality, with cumulative explanatory ratios of 67.3% and 50.1%. This study utilized land-use change as a metric for human activities, highlighting the potential impacts of climate change and human activities on the water quality of Xingkai Lake. It offers vital insights for the sustainable management of Xingkai Lake and provides valuable references into the management of similar transboundary lakes.

Relationship between water quality and phytoplankton distribution of aquaculture areas in a tropical lagoon.

Aquaculture activities can affect water quality and phytoplankton composition. Our study estimated phytoplankton density and composition relating to aquaculture-impacted environmental factors. We analyzed water quality and phytoplankton at 35 sites in a tropical brackish lagoon, including inside aquaculture ponds (integrated farming of fish, shrimp, and crab), at wastewater discharge points, within 300m of these points, and farther out in the lagoon. Measurements were taken after aquaculture activities started in March and again in July. In both periods, total nitrogen (TN), total phosphorus (TP), chlorophyll-a (Chl-a), and turbidity decreased from the aquaculture ponds to the farther lagoon areas. Principal component analysis showed that nutrients, turbidity, and Chl-a were critical factors in aquaculture ponds, while salinity, temperature, pH, dissolved oxygen (DO), and water depth influenced water quality outside the ponds. Phytoplankton density was higher in July than in March due to aquaculture characteristics. Redundancy analysis indicated that phytoplankton, typical of inorganic, turbid, shallow lakes, was present throughout, whereas marine phytoplankton characterized the open water area (OWA). Marine phytoplankton caused a higher Shannon-Wiener index in July compared to March for OWA. Phytoplankton in aquaculture ponds was dominated by Oscillatoria spp., while Thalassiosira spp. dominated outside the ponds. We also identified indicator genera for two connected lagoons. Although constant water exchange prevented identifying specific indicator phytoplankton groups for aquaculture, this revealed the impact of wastewater from aquaculture ponds on the natural environment in the lagoons. Research on phytoplankton communities is necessary for the sustainable development of aquaculture and environmental management in coastal lagoons.

Characteristics of Nitrogen Removal from an Integrated Fixed-Film Activated Sludge (IFAS) System and the Relationship Between Activated Sludge and Biofilm Interactions

In order to investigate the enhancement mechanism of modified three-dimensional elastic filler (MTEF) on the nitrogen removal performance of the integrated fixed-film activated sludge (IFAS) process, and to clarify the interactions between competition and synergy between activated sludge and biofilm in the IFAS system, an IFAS reactor (T2) filled with MTEF was employed for the study, while a sequencing batch reactor activated sludge process (SBR) reactor (T1) was utilized for comparison. IFAS and SBR reactors were operated over an extended period at ambient temperature to assess the enhancement of pollutant removal performance with the addition of the filler to investigate the competitive dynamics between activated sludge and biofilm under varying influent water qualities (C/N, N/P, and organic loading), and to analyze the synergistic relationship between activated sludge and biofilm at the microbial level using high-throughput sequencing technology. The results demonstrate that throughout the entire operational phase, reactor T2 exhibited superior pollutant removal efficiency. Compared to reactor T1, reactor T2 achieved an average increase in the removal rates of COD, ammonia nitrogen, and total nitrogen by 13.07%, 12.26%, and 28.96%, respectively. The findings on the competitive dynamics between activated sludge and biofilm indicate that the nitrification volumetric load of the IFAS system is significantly higher than that of a pure activated sludge system, suggesting that the IFAS system possesses enhanced nitrification capabilities. Furthermore, when dealing with wastewater characterized by low C/N ratios and high phosphorus pollution, or under substantial organic loads, the biofilm holds a competitive edge and the IFAS system exhibits improved stability. High-throughput sequencing data reveal that the microbial community structures in activated sludge and biofilm can influence each other, thereby enabling the IFAS system to effectively enrich denitrification-related functional microbial populations. Additionally, the biofilm has a certain enhancing effect on the expression levels of nitrogen metabolism-related functional genes in the activated sludge phase microorganisms, indicating that, in addition to competitive interactions, there is also a synergistic effect between the biofilm and activated sludge.

Divergent roles of landscape and young streamflow fraction in stream water quality over seven catchments

Landscape patterns and water age are considered to play similar roles in influencing water quality, as human-caused landscape fragmentation usually leads to complex hydrological pathways and older water ages. The young streamflow fraction (Fyw), which can significantly alter water age, is believed to disproportionately impact water quality. Landscape and Fyw thus may play different roles in streamwater quality, but this assumption has not been examined. Here we examined the roles of Fyw (estimated by the amplitudes of seasonal cycles of oxygen isotope ratios in precipitation (Ap) and streamwater (As)) and landscape pattern (reflected by common landscape metrics) in streamwater quality over 7 forest-dominated catchments in eastern China. Landscape metrics, indicating land use arrangement, were closely related to multiple water quality parameters, suggesting a close association between stream water quality (sinks) and land use (sources). The Fyw had a positive relationship with ammonia nitrogen (R2 = 0.51, P = 0.044) and a negative relationship with nitrate nitrogen (R2 = 0.62, P = 0.022). The Fyw, determined by the heterogeneous-independent As/Ap, offers the possibility of modeling nitrogen compounds across multiple spatial scales. Importantly, the water quality parameters that significantly correlated with Fyw did not correlate with the landscape metrics. Differences in correlations of landscape and Fyw to water quality parameters imply that landscape and Fyw differed in their roles in water quality parameters, as well as the consideration for different water quality management strategies for different contaminants.

Response of Water Quality to Land Use and Landscape Pattern in the Ganjiang River Watershed.

Analysing the impact of landscape composition and structure on water quality at different scales is of great significance to water quality protection. The aim of this study was to determine scale-dependent impacts of land use/landscape patterns on water quality. The Ganjiang River, the largest water system in the Poyang Lake watershed, the largest freshwater lake in China. The response of water quality to land use and landscape patterns in the Ganjiang River watershed was explored based on land use and water quality data using redundancy and Spearman correlation analyses. Considering upstream monitoring of the entire Ganjiang River watershed; watersheds at the county level administrative region; and 1, 2, 5, 10, 15, 20, and 30 km-radius circular buffer zones, a total of nine scales of land use/landscape patterns that influence water quality in the Ganjiang River watershed were analysed. Results indicated that the county-level scale and the land use type of the 20 km-radius buffer zone upstream of the monitoring site were closely linked to water quality (96.28% and 93.23%, respectively). Among the land use types, construction land and cultivated land were the main output sources of pollutants. Regarding landscape pattern index, the greater the fragmentation of the landscape, the heavier was the water pollution load; the more the patches per unit area, the more stable was the ecosystem and the lower was the pollutant concentration. In addition, the eco-hydrological system of the Ganjiang River watershed was revealed to some extent through multi-angle analysis. These conclusions can serve as a reference for government departments to formulate land management and water quality protection measures.

Demonstrating the Underestimated Effect of Landscape Pattern on Total Nitrogen and Total Phosphorus Concentrations Based on Cellular Automata–Markov Model in Taihu Lake Basin

The expanding cropland profoundly affects stream water quality. However, the relationships between landscape patterns and stream water quality in different cropland composition classes remain unclear. We observed total nitrogen (TN), total phosphorus (TP) concentrations, and landscape patterns changed in 78 sub-watersheds of the Taihu Lake Basin’s Jiangsu segment from 2005 to 2020. The results showed that cropland area was positively correlated with TN and TP concentrations. The 21.10% reduction in cropland area, coupled with a 41.00% increase in building land, has led to an escalation in cropland fragmentation. Meanwhile, TN and TP concentrations declined by 26.67% and 28.57%, respectively. Partial least squares suggested that forest interspersion and juxtaposition metrics and forest area percentage were dominant factors influencing water quality in high- and medium-density cropland zones, respectively. The Cellular Automata–Markov Model shows reasonable distribution of forests. Scenarios with enhanced forest interspersion and juxtaposition metrics (75.28 to 91.12) showed reductions in TP (26.92% to 34.61%) and TN (18.45% to 25.89%) concentrations by 2025 compared to a natural economic development scenario. Landscape configuration optimization could assist managers in improving water quality.

Impact of Landscape Patterns on Water Quality in Urbanized Rivers at Characteristic Scale: A Case of Pearl River Delta, China.

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers.

Tidal influence on Fish Faunal Distribution and Species Diversity in Kakum Estuary, Ghana

Estuarine and coastal areas are complex and dynamic aquatic environments. In such an area, where river water mixes with seawater, a large number of physical and chemical processes take place, which may influence water quality thereby affecting the distribution and diversity of the biota. The study was conducted in the transition period of dry and wet conditions from February to May 2018. The influence of low and high tides on the hydrographic parameters such as salinity, dissolved oxygen, and water temperature in the three sampling stations was analyzed in relation to fish faunal distribution and species diversity of the estuary. The common fish species in the estuary were juveniles of Sarotherodon melanotheron and Mugil cephalus, Liza falcipinnis and the crab Callinectes amnicola. These species were encountered during both low and high tides in all sampling stations, which indicates their adaptation to a broader range of hydrographic conditions. The majority of species encountered were juveniles. Although most of the fish species were marine in origin, some including Tilapia zillii, Hemichromis fasciatus and Apolochelichthys spilauchen were freshwater in origin whilst, S. melanotheron is a resident fish species in the estuary. Length-frequency distribution and length-weight relationships of S. melanotheron, T. zillii, L. falcipinnis, Mugil cephalus, and Eucinosthomus melanopterus which were the five most dominant fish species during the period of study in Kakum estuary were analyzed. The modal sizes of the dominant fish species were mainly juveniles in the length-frequency distribution. The length-weight relationships of S. melanotheron, L. falcipinnis and M. cephalus showed allometric growth, whereas that of T. zillii and E. melanopterus showed isometric growth. The study, therefore, identified that Kakum estuary is a transitional zone for both marine and freshwater fish species using the estuarine ecosystem as nursery grounds. The presence of marine and freshwater-origin species, and resident species in the estuary further suggest that estuaries are highly dynamic, and slight changes in their environment can cause a great deal of harm to its fish biota.

Comparison of periphyton growth on two artificial substrates in temperate zone fishponds

Periphyton communities are fundamental components of freshwater ecosystems that influence water quality, nutrient cycling, and productivity. Additionally, periphyton communities serve as valuable food sources in aquaculture. The periphyton communities on two substrates—heather mats and geotextiles—were compared in four temperate fishponds. After 51 days of immersion, the geotextiles exhibited significantly greater fluorescence, indicating a greater abundance of algae and cyanobacteria. Conversely, the abundance (and biomass) of periphyton invertebrates on heather mats was greater than that on geotextiles, and the difference became more pronounced over time. These differences resulted from significantly greater dipteran larval abundances (biomasses) on the heather mats than on the geotextiles. This study suggested that heather mats can significantly enhance food abundance and availability within the water column under Central European conditions.

Watershed landscape characteristics and connectivity drive river water quality under seasonal dynamics

Landscape characteristics and connectivity are pivotal determinants of river water quality within the context of water quality management. They play particularly important roles in the spread of non-point source pollution. However, previous research has not investigated their combined effects, especially in the topographically and ecologically volatile Loess Plateau, China. This study applied machine learning and positive matrix factorization techniques to water quality monitoring data for the Wuding River basin, China, from 2017 to 2021 to assess the seasonal impacts of environmental factors on river water quality. The results showed that there were spatial and seasonal variations in pollution sources, and specific thresholds for the key landscape indices that influence water quality were identified. Landscape composition and configuration have the greatest influence on the water quality parameters during the dry season, whereas connectivity and landscape configuration are more critical during the wet season. The connectivity contributions to chemical oxygen demand, the potassium permanganate index, and total phosphorus in the wet season were 19.07%, 27.47%, and 5.08% greater than in the dry season, respectively. Connectivity also made a significant contribution to the composite water quality index, accounting for 33.46% in the dry season and 36.22% in the wet season. The positive matrix factorization model identified agricultural non-point sources and mixed pollutants as the main factors affecting water quality during the dry season, whereas erosion and mixed pollution sources dominated the wet season. Critical thresholds were established for the landscape indices that affected water quality, such as a landscape shape index below 18.5 and a grassland proportion above 65%. The results can be used to develop more effective water quality management strategies and illustrate the essential role played by connectivity when tailoring water quality management strategies to seasonal variations.

Lite approaches for long-range multi-step water quality prediction

Forecasting accurate water quality is very important in aquaculture, environment monitoring, and many other applications. Many internal and external factors influence water quality. Therefore, water quality parameters exhibit complex time series characteristics. Consequently, long-range accurate prediction of water quality parameters suffers from poor propagation of information from past timepoints to further future timepoints. Moreover, to synchronise the prediction model with the changes in the time series characteristics, periodic retraining of the prediction model is required and such retraining is to be done on resource-restricted computation devices. In this work, we present a low-cost training approach to improve long-range multi-step water quality prediction. We train a short-range predictor to save training effort. Then, we strive to achieve and/or improve long-range prediction using multi-step iterative ensembling during inference. Experimental results on 9 water quality datasets demonstrate that the proposed method achieves significantly lower error than the existing state-of-the-art approaches. Our approach significantly outperforms the existing approaches in several standard metrics, even in the case of future timepoints at long distances.

Multisystemic inflammatory disease in Pheasantshell (Unionidae, Actinonaias pectorosa) associated with Yokenella regensburgei infection at sites experiencing seasonal mass mortality events.

Freshwater mussels are integral components of riverine ecosystems, influencing water quality, nutrient cycling, and habitat characteristics. Enigmatic freshwater mussel declines, often characterized by sudden mass mortality events, pose significant challenges to conservation efforts. The Clinch River, a freshwater biodiversity hotspot in Virginia and Tennessee, USA, has experienced several enigmatic mass mortality events since 2016. Studies have reported bacteria associated with moribund Pheasantshell (Actinonaias pectorosa) during mortality events in the Clinch River, specifically Yokenella regensburgei. Despite reports of bacterial infection, little is known about their role as pathogens. Through a multiyear case-control study, combining in-situ experiments, field surveys, histology, bacterial isolation, and high-throughput sequencing, we assessed the role of bacteria in Pheasantshell (Actinonais pectorosa) mortality at three sites in the Clinch River. Between May 2021 and December 2023, we collected 21 wild moribund free-living A. pectorosa and 68 hatchery-reared A. pectorosa maintained in silos at the same sites and investigated differences in pathology and microbiologye between groups. No silo mussels presented clinical signs of disease, or gross or microscopic lesions associated with pathological conditions leading to mortality. Our findings reveal a significant association between Yokenella regensburgei and severe multisystemic and multifocal infiltrative hemocytosis with necrosis, consistent with sepsis. Lesions associated with yokenellosis were of sufficient severity and physiological significance to explain mortality in infected hosts. Although our study does not explain the cause of these infections, it confirms that mussels at our study sites are ultimately dying with an infectious disease and that Y. regensburgei can be pathogenic in free-living mussels. Our results underscore the importance of considering bacterial diseases in wild mussel populations and emphasize the need for further research to elucidate the epidemiology and pathogenicity of Y. regensburgei. Overall, our study highlights the importance of integrated approaches combining pathology, microbiology, and epidemiology in freshwater mussel conservation efforts.

Offset integrity reduces environmental risk: Using lessons from biodiversity and carbon offsetting to inform water quality offsetting in the catchments of the Great Barrier Reef

Environmental offsetting has been developed as a mechanism to facilitate the benefits from economic development while avoiding or minimizing environmental harm. This is achieved by compensating for environmental impacts at one location by generating equivalent environmental improvements elsewhere. However, experience with biodiversity and carbon offsetting indicates it can be difficult to ensure the integrity of offsets. Under recent legislation in the catchments of the Great Barrier Reef (GBR), Australia, it is mandatory for water quality emissions from new or expanded point source development to be offset by reducing pollution elsewhere, frequently through reducing non-point source pollution (NPSP). Therefore, informed by experience with biodiversity and carbon offsetting, we summarised sources of uncertainty in NPSP reduction that would influence water quality offset integrity; estimated the maximum potential demand for water quality offsets from sewage treatment plants, the largest point source emitter of total nitrogen (TN) in the GBR catchments, between 2018 and 2050; and discussed the implications of both on the ability of offsetting to counterbalance the impact of economic development in catchments where nitrogen loads have a large influence on the health of important GBR ecosystems. The catchments surrounding the population centres of Cairns and Mackay had both a potentially high future demand for nitrogen water quality offsets and nitrogen loads with a strong influence on the health of the GBR. Consequently, any low integrity water quality offsets in these catchments could jeopardise progress toward the water quality improvements needed to ensure the continued health of the GBR. Water quality offsetting has numerous strengths as a policy instrument however substantial uncertainties remain related to environmental outcomes. Until further research can reduce these uncertainties, water quality offsets that are implemented near increased point source emissions and have a high certainty of effectiveness may provide a balance between scientific rigour and policy workability.

Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors

Efficient management of wastewater treatment plants (WWTPs) necessitates accurate forecasting of influent water quality parameters (WQPs) and flow rate (Q) to reduce energy consumption and mitigate carbon emissions. The time series of WQPs and Q are highly non-linear and influenced by various factors such as temperature (T) and precipitation (Precip). Conventional models often struggle to account for long-term temporal patterns and overlook the complex interactions of parameters within the data, leading to inaccuracies in detecting WQPs and Q. This work introduced the Pre-training enhanced Spatio-Temporal Graph Neural Network (PT-STGNN), a novel methodology for accurately forecasting of influent COD, ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), pH and Q in WWTPs. PT-STGNN utilizes influent data of the WWTP, air quality data and meteorological data from the service area as inputs to enhance prediction accuracy. The model employs unsupervised Transformer blocks for pre-training, with efficient masking strategies to effectively capture long-term historical patterns and contextual information, thereby significantly boosting forecasting accuracy. Furthermore, PT-STGNN integrates a unique graph structure learning mechanism to identify dependencies between parameters, further improving the model's forecasting accuracy and interpretability. Compared with the state-of-the-art models, PT-STGNN demonstrated superior predictive performance, particularly for a longer-term prediction (i.e., 12 h), with MAE, RMSE and MAPE at 12-h prediction horizon of 2.737 ± 0.040, 4.209 ± 0.060 and 13.648 ± 0.151 %, respectively, for the algebraic mean of each parameter. From the results of graph structure learning, it is observed that there are strong dependencies between NH3-N and TN, TP and Q, as well as Precip, etc. This study innovatively applies STGNN, not only offering a novel approach for predicting influent WQPs and Q in WWTPs, but also advances our understanding of the interrelationships among various parameters, significantly enhancing the model's interpretability.

An Integration Method of UML Diagrams and Arduino UNO for Developing Water Pollution Detection System

Despite the scientific and health development in the world, water pollution can pose a threat to human health, whether the pollutants are biological or chemical. In addition, traditional methods of water pollution detecting require high costs, and take time. Due to the importance of water for human life, drinking, irrigation, or for use in industry etc. Thus, reliable methods for water contamination detecting are a crucial issue.This paper aims to propose an integration method of UML diagrams and Arduino UNO microcontroller for developing water pollution detection system. The proposed method consists of three phases, which are system’s design, system's schematic and system’s architecture. It uses use case diagram and sequence diagram. In addition, the system design consisting of a microcontroller and multiple connected sensors for water pollutant detection, namely pH, turbidity, dissolved solids, temperature, oxidation reduction potential and electrical conductivity. The result of this paper is the production an automatic water pollutant detection prototype using Arduino and UML diagrams to display the water pollution parameters. The variables pH, turbidity, dissolved solids, temperature, oxidation reduction potential and electrical conductivity were chosen because they are the physical and chemical parameters that most influence water quality. The proposed system is characterized by measuring the water quality in real time, low cost and its data is accurate and fast.

Implications of Water Quality Index and Multivariate Statistics for Improved Environmental Regulation in the Irtysh River Basin (Kazakhstan)

The selection of sites for permanent environmental monitoring of natural water bodies should rely on corresponding source apportionment studies. Tools like the water quality index (WQI) assessment may support this objective. This study aims to analyze a decade-long dataset of measurements of 26 chemical components at 26 observation points within the Irtysh River Basin, aiming to identify priority zones for stricter environmental regulations. It was achieved through the WQI tool integrated with geoinformation systems (GISs) and multivariate statistical techniques. The findings highlighted that both upstream sections of tributaries (Oba and Bukhtarma rivers) and the mainstream of the basin are generally in good condition, with slight fluctuations observed during flooding periods. Areas in the basin experiencing significant impacts from mining and domestic wastewater treatment activities were identified. The rivers Glubochanka (GL) and Krasnoyarka (KR) consistently experienced marginal water quality throughout the observation period. Various contaminant sources were found to influence water quality. The impact of domestic wastewater treatment facilities was represented by twofold elevated concentrations of chemical oxygen demand, reaching 22.6 and 27.1 mg/L for the KR and GL rivers, respectively. Natural factors were indicated by consistent slight exceedings of recommended calcium levels at the KR and GL rivers. These exceedances were most pronounced during the cold seasons, with an average value equal to 96 mg/L. Mining operations introduced extremal concentrations of trace elements like copper, reaching 0.046–0.051 mg/L, which is higher than the threshold by 12–13 times; zinc, which peaked at 1.57–2.96 mg/L, exceeding the set limit by almost 50–100 times; and cadmium, peaking at levels surpassing 1000 times the safe limit, reaching 0.8 mg/L. The adverse impact of mining activities was evident in the Tikhaya, Ulba, and Breksa rivers, showing similar trends in trace element concentrations. Seasonal effects were also investigated. Ice cover formation during cold seasons led to oxygen depletion and the exclusion of pollutants into the stream when ice melted, worsening water quality. Conversely, flooding events led to contaminant dilution, partially improving the WQI during flood seasons. Principal component analysis and hierarchical cluster analysis indicated that local natural processes, mining activities, and domestic wastewater discharge were the predominant influences on water quality within the study area. These findings can serve as a basis for enhanced environmental regulation in light of updated ecological legislation in Kazakhstan, advocating for the establishment of a comprehensive monitoring network and the reinforcement of requirements governing contaminating activities.

Evaluating the spatiotemporal variation of Ba River water quality in the agricultural and urban watershed in the highland of Vietnam.

The Ba River in Vietnam has been facing pollution due to waste generation from agricultural and urban areas. This study focuses on evaluating the spatiotemporal variations in river water quality based on physicochemical characteristics and pesticide parameters for different seasons in 2022-2023. The results indicate that the concentrations of most parameters in the rainy season were higher than those in the early-dry and dry seasons due to the non-point sources in agricultural areas. Notably, the analysis of pesticide residue in both the rainy and dry seasons revealed low levels of chlorpyrifos (ethyl), and deltamethrin was detected in the only rainy season. The results from the hierarchical cluster analysis and water quality index show that the water quality at Ben Mong, An Khe, and Ba River Bridges was classified as moderately to highly polluted. These areas should focus on regular water quality monitoring and appropriate pollution source management. PRACTITIONER POINTS: Agriculture activities strongly affected the water quality of the Highland Ba River of Vietnam. Chlorpyrifos and deltamethrin pesticides (0.0074-0.0218 μg/L) were detected in Ba River. Non-point pollution sources significantly influenced water quality in the Ba River. Variations in river water quality mainly depend on seasons and locations. Water quality index values in rainy seasons (26-88) are lower than that in dry season (37-92).

The role of land use on phosphorus release and longitudinal changes of pollution in an agricultural watershed, Bostankar river, Iran

Phosphorus in surface waters accelerate algal growth and eutrophication, considerably influencing water quality. Spatiotemporal changes in phosphorus concentration are crucial for environmental issues. We aimed to study the temporal and spatial changes in water quality in a river and in a drainage water system considering different land uses. To this aim, 15 water samples were collected from the origin of the river to the estuary, in the Bostankar River watershed (N-Iran), during spring and winter. Further samples were collected from agricultural drainage water in rice fields, tea, flower, orange as well as kiwi gardens, and forests during spring and winter. EC, pH, TDS, and three forms of phosphorus (total, particulate, and soluble) were measured in the water samples. The results showed that water quality changes in agricultural drainage water were time-dependent; the average total phosphorus was 0.4 mg l-1 lower in the spring than in the winter. The highest phosphorus concentration (1.29 mg l-1) occurred in the winter in the drainage water of the orange gardens. Temporal and spatial changes of the river showed that water quality reduced from the river upstream (jungles and grasslands) towards the downstream (different agricultural land uses), and the amount of phosphorus increased from 0.25 to 0.5 mg l− 1. The TDS increased from 60 to 220 mg l− 1 in the river in the winter. Finally, the results showed that human activities were the main factor in river water quality reduction due to agricultural activities.