Water Quality Variables Research Articles

To the Studies on Pollution of Under Ground Water Mau City

The study focuses on the environmental evaluation and classification of the Mau City area, located in the eastern part of Uttar Pradesh, India. The investigation covers an area of approximately 20 km2 and includes a diverse population in terms of socio- economic, cultural, and geographical characteristics. The population data reveals significant growth, with a population increase from 168,716 in 1991 to 2,205,968 in 2011. Animal populations, including cattle and poultry, have also shown an increasing trend from 2018 to 2020. Fertilizer usage data between 2017 and 2020 indicates fluctuations in the consumption of nitrogen, phosphorus, and potassium, demonstrating potential agricultural impacts on the environment. Water quality analysis is a major part of this study, employing various methods to determine salinity, dissolved oxygen, biological oxygen demand (BOD), and chemical oxygen demand (COD). Salinity was measured through titration with silver nitrate, while oxygen levels were analyzed using different methods, including dissolved oxygen (DO) analysis. Results indicate variations in water quality across different sampling sites (S1-S6), with key parameters such as pH, turbidity, temperature, and bacterial contamination being recorded monthly for the years 2019 and 2020.The research also involves the collection and analysis of water samples using various devices, including water samplers, bottom samplers, and biological samplers. Methods such as the EDTA method for water hardness, the gravimetric method for sulfate determination, and the colorimetric analysis for iron and chromium concentrations were utilized to evaluate the pollutants in the water. The study provides critical insights into water quality degradation, highlighting the presence of various pollutants, including nitrates, sulfates, and heavy metals like chromium, copper, and iron.

Estimating the vertical profile of water quality variables in reservoirs: Application of remotely sensed data and machine learning techniques.

Water quality assessment and management of reservoirs depend on accurate, large-scale, and continuous monitoring of the vertical profile of Water Quality Variables (WQVs). Remote sensing data have been widely used to retrieve high spatiotemporal water quality data; however, their application has practically been limited to evaluating surface WQVs. In this paper, a novel and efficient approach is introduced for assessing the profile of WQVs in reservoirs that depend on stratification, by taking into account the shape of profile as prior knowledge. First, an appropriate function is fitted to the WQVs vertical profile, and second, the parameters of that function as representative of the WQVs vertical profile are estimated using machine learning techniques. The model's inputs are day, maximum depth of point, and remote sensing data. Finally, PAWN sensitivity analysis is applied to show the extent to which each input influences different parts of the vertical profile. This method is applied in the Wadi Dayqah Reservoir, the largest dam in Oman, to evaluate water temperature, dissolved oxygen, pH, and chlorophyll-a profile. The results show that the predicted profiles are properly representative of in situ measurements, with a mean absolute error of 0.28 °C, 0.25 mg/L, 0.052, and 0.33 μg/L on test data sets of water temperature, dissolved oxygen, pH, and chlorophyll-a, respectively. Finally, PAWN sensitivity analysis illustrates that satellite data not only influence the parameters representing surface WQVs but also contribute to the estimation of other curve parameters.

Short-term effects of a research-scale oyster cage aquaculture system on sediment transport, water quality, and seagrass meadow health in Copano Bay, TX, USA

Competing uses for nearshore coastal space, such as aquaculture, have complex environmental and ecological interactions with surrounding seagrass meadows. Oyster aquaculture is among the fastest-growing aquaculture segments in the United States, and it brings concerns such as increased sedimentation from farm maintenance operations and altered water quality through oyster filtration. Changes in seagrass coverage and growth are common indicators of ecological health used to determine anthropogenic impacts on nearshore environments. This study characterized the effects of a research-scale adjustable long-line oyster aquaculture system on the health of adjacent seagrass meadows in Copano Bay, TX. Four Halodule wrightii meadows were identified at various distances from the research site: 100 m upstream (upstream), directly adjacent (0 m), 30 m downstream (30 m), and 60 m downstream (60 m). Sites were monitored for 1) seagrass health, 2) water quality parameters, and 3) sediment deposition. Over the 18-week sampling period, no significant differences (p>0.05) were found for water quality variables or sediment grain-size analysis using a one-way ANOVA. A linear mixed-effects model was used for repeated measures of seagrass data, with no effect of the site found on mean seagrass length, coverage, or maximum length (p>0.05). These results suggest that seagrass health indicators were unaffected by proximity to the oyster system. It was concluded that an oyster research aquaculture system of the type and size at the project location had no major negative or positive impact on seagrass meadow health due to high background variability and the overall minor footprint of the oyster farm across a comparatively large spatial extent.

Water quality in the Chinese Qinling Mountains over the last 20 years

The Qinling Mountains, the so-called “central water tower,” are extremely important water resource hubs in China. The influence of the forest ecological environment on water quality is complicated. Therefore, to investigate the spatiotemporal variations in water quality, we employed a random forest model to integrate multiple key water quality indicators into one overall ranking value. Monthly monitoring data of surface runoff and atmospheric precipitation events (2003–2022) for the Huodigou stream in the Qinling Mountains were used. The results revealed that after atmospheric precipitation entered the forest ecosystem, the coefficients of variation of surface runoff for most of the selected indicators decreased, but there were significant differences among the six indicators (NO3−, Mg2+, Na+, pH, K+, Ca2+). Most of the indicators within surface runoff were positively correlated, such as those in atmospheric precipitation. However, some indices of surface runoff were negatively correlated with those of atmospheric precipitation, and there was a significant negative correlation between Ca2+ in atmospheric precipitation and Ca2+ in surface runoff and between NO3−in atmospheric precipitation and K+ and Na+ in surface runoff (p < 0.01). The water quality grade of the surface runoff generated by atmospheric precipitation through forest ecosystems was significantly improved (p < 0.001), among which the average water quality grade of surface runoff was approximately 3.6, that is, between Grade I-3 and Grade I-4, whereas the average water quality grade of atmospheric precipitation was approximately 4.5, that is, between Grade I-4 and Grade I-5. The order of improved water quality was NO3− > Mg2+ > Na+ > pH > K+ > Ca2+. Overall, our assessment revealed that from 2003 to 2022, the water quality grade in the Huodigou stream improved and was more stable. In summary, the forest ecosystem in the Huodigou stream has a significant water quality purification effect on the atmospheric precipitation it receives. Our novel criterion-based approach for categorizing the water quality of atmospheric precipitation and surface runoff offers a new tool for examining spatiotemporal stream water quality variations in the Qinling region and other mountainous areas.

A novel approach for multivariate time series interval prediction of water quality at wastewater treatment plants

ABSTRACT This study proposes a novel approach for predicting variations in water quality at wastewater treatment plants (WWTPs), which is crucial for optimizing process management and pollution control. The model combines convolutional bi-directional gated recursive units (CBGRUs) with Adaptive Bandwidth Kernel Function Density Estimation (ABKDE) to address the challenge of multivariate time series interval prediction of WWTP water quality. Initially, wavelet transform (WT) was employed to smooth the water quality data, reducing noise and fluctuations. Linear correlation coefficient (CC) and non-linear mutual information (MI) techniques were then utilized to select input variables. The CBGRU model was applied to capture temporal correlations in the time series, integrating the Multiple Heads of Attention (MHA) mechanism to enhance the model's ability to comprehend complex relationships within the data. ABKDE was employed, supplemented by bootstrap to establish upper and lower bounds of the prediction intervals. Ablation experiments and comparative analyses with benchmark models confirmed the superior performance of the model in point prediction, interval prediction, the analysis of forecast period, and fluctuation detection for water quality data. Also, this study verifies the model's broad applicability and robustness to anomalous data. This study contributes significantly to improved effluent treatment efficiency and water quality control in WWTPs.

Spatiotemporal Dynamic Analysis of Eutrophication Status Based on Machine Learning-Based Retrieval Algorithm: Case Study in Liangzi Lake, Hubei, China

The routine monitoring of eutrophication is an important measure for observing the variation in water quality and protecting the ecological health of lakes. However, in situ information reflects eutrophication levels within a limited distance and period. In this study, we retrieved the trophic level index (TLI) based on Landsat 8 remote sensing images and using a machine learning (ML) method in Liangzi Lake in Hubei Province, China. The results showed that random forest (RF) outperformed other ML algorithms in estimating the TLI, evaluated by its higher fitness through the Monte Carlo method (median values of R2, RMSE, and MAE are 0.54, 0.047, and 0.037, respectively). In general, 8% of the areas of Liangzi Lake presented an increasing eutrophication level from 2014 to 2022, and 20.1% of the areas reached a mild eutrophication level in 2022. In addition, we found that temperature and anthropogenic activities may impact the eutrophication conditions of the lake. This work uses remote sensing imagery and a ML method to monitor the dynamics of the lake’s eutrophication status, thereby providing a valuable reference for pollution control measures and enhancing the efficiency of water resource management.

Spatial variation in water quality of the Burhi Gandak River: a multi-location assessment

The Burhi Gandak River, a significant tributary of the Ganga River and a vital water source in Bihar, India, is facing critical water quality degradation due to rapid population growth, urbanization, and industrial activities. This study conducts a detailed water quality assessment of the river, focusing on the effects of these anthropogenic factors across different locations, seasons, and industrial zones. Water samples were collected and analyzed for key parameters, including pH, total dissolved solids (TDSs), total hardness (TH), biological oxygen demand (BOD), and dissolved oxygen (DO). The results indicate significant spatial and temporal variations, with water quality deteriorating notably in areas like Samastipur and Khagaria, where industrial and urban activities are more concentrated. For instance, BOD levels increased from 7.5 mg/L to 9.5 mg/l as the river flows through urban Samastipur, signaling a decline in water quality. Additionally, sugar mills located along the river contribute to higher pollution levels during operational months, especially in Lauria and Sugauli. Seasonal analysis shows that the lean season experiences the highest levels of degradation, while monsoon floods increase suspended solids due to sediment inflow. These findings emphasize the urgent need for improved regulation of industrial discharge and urban waste management to protect the water quality of the Burhi Gandak River and the livelihoods dependent on it.

Analysis of Variability of Water Quality Indicators in the Municipality Water Supply System—A Case Study

This study investigated the variability of water quality indicators in four municipal water distribution systems near a medium-sized city. Despite the proximity of water intakes, water quality in different distribution systems can vary significantly due to local factors such as infrastructure conditions, treatment technology, and specific environmental conditions affecting water in each water supply network. Water samples were collected from multiple points in each system and analyzed for physicochemical properties. The results showed significant differences in total carbon, dissolved organic carbon, and ammonium nitrogen, indicating variability in water quality between systems. These results emphasize the need for integrated management strategies, innovative technologies, and real-time monitoring to maintain water quality. The study also highlights challenges such as aging infrastructure, pollution, and financial constraints in managing water supplies.

Bacteriology and Physicochemical Analysis of Borehole Water in Some Hostels of a Tertiary Institution in Port Harcourt, Nigeria

Several factors influence the quality of water for human consumption. Most of these factors are more of anthropogenic origin than nature and thus exercises an untold health effect on the end-users of such water body. Bacteriology and Physicochemical Analysis of Borehole Water in Some Hostels of a Tertiary Institution in Port Harcourt, Nigeria. Water samples were collected from six (6) hostels for each study period, and subjected to standard laboratory procedures to analyze for parameters such as pH, nitrite, nitrate, dissolved oxygen (DO), electrical conductivity (EC), Phosphate, as well as standard procedures for antibiotics susceptibility and molecular characterization. Result of the physicochemical parameters showed that the parameters ranged from 5.26±3.53 mg/L to 7.61±0.01 mg/L forpH; 0.61±0.01 to 2.56±0.01 mg/L for Dissolved Oxygen (DO); 96±1.41 μS/cm to 503.5±0.71μS/cm for electrical conductivity; 4.56±0.03 mg/L to 20.3±0.01 mg/L for nitrate; 0.003±0mg/L to 0.03±0mg/L for nitrite; 0.12±0mg/L to 0.17±0mg/L for phosphate. Statistical evaluation showed there was a significant difference (p < 0.05) between the mean values of physicochemical parameters studied, except for pH that recorded no statistical difference (p > 0.05). From the study, 5.6% of Staphylococcus species were resistant to Pefloxacin and Rocephin, while 5.9% of Bacillus species were resistant to Ampiclox. Similarly, 3.33% of Salmonella species were resistant to Augmentin, while the rest of the gram negative isolates were either susceptible or within an intermediate range of susceptibility to the antibiotics tested. Molecular characterization of some bacterial species isolated had pairwise identity with Klebsiella pneumonia strain CP27 and Escherichia coli strain RIZHAO 498.1, Staphylococcus haemolyticus strain SH1. The study has shown that the physicochemical water quality variables were all within the WHO limits, with bacterial species having high percentage susceptibility to the antibiotics tested. Regular water quality monitoring is however recommended to ensure continuous access to water of good quality in hostels within the university community.

Evaluation of SDGSAT-1 MII data in total suspended matter estimation in clear to extremely turbid rivers

ABSTRACT The Multispectral Imager (MII) onboard the newly launched SDGSAT-1 holds significant promise for monitoring and understanding variations in water quality within nearshore water systems, with an impressive spatial resolution of 10 m, seven spectral bands in the visible and near-infrared domain and a wide swath of 300 km. This paper evaluates the MII data in estimating the concentration of total suspended matter (TSM) in rivers and estuaries from clear to extremely turbid conditions. For atmospheric correction, the ACOLITE model showed superior performance compared to the FLAASH and 6SV, with average unbiased relative error (AURE) ranging from 11.8% to 26.4% in the four visible bands of MII. Through comparison, a TSM model based on the visible band ratio (i.e., Rrs (660)/Rrs (560)) was proposed to ensure the application of MII data in both clear and turbid waters, with an overall AURE of 40.9%. The model was then applied to map the TSM variations in the Hai and Liao Rivers in northern China using the MII data, where reliable spatial and temporal patterns were observed. The study concludes by discussing the applicability of MII data in water quality mapping in nearshore waters, along with key considerations such as model applicability, atmospheric correction, and land adjacency.

Machine Learning Based Inversion of Water Quality Parameters in Typical Reach of Rural Wetland by Unmanned Aerial Vehicle Images

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas.

Comparative analysis of habitat structure and macroinvertebrate assemblages in headwater streams of Odzi sub-catchment, Chimanimani District, Zimbabwe, post Cyclone Idai-induced flooding

Flooding results in short and long-term modifications of aquatic ecosystem structure and biodiversity. Cyclone Idai, which ravaged through Zimbabwe in 2019, had devastating consequences on aquatic ecosystems. However, the extent of habitat modification and consequent biodiversity shifts are unknown. This study focuses on the comparative analysis of (i) habitat structure and (ii) macroinvertebrate assemblages in headwater streams of the Odzi sub-catchment, Chimanimani District, Zimbabwe, specifically examining the conditions post-Cyclone Idai-induced flooding. Field sampling was conducted in the dry season (September−November) 2019 and wet season (January−March) 2020. Headwater streams of the Murare (first order), Umvumvumvu (first and second order) and Nyanyadzi (first and second order) rivers were sampled. Habitat assessment was done following the Benthic Macroinvertebrate Monitoring Protocol Implementation Plan. Selected water variables were measured using appropriate probes. Macroinvertebrate assemblage data were collected using the South African Scoring System (SASS 5) protocol. Spatiotemporal variations in water quality, habitat structure and macroinvertebrate metrics were assessed using two-way ANOVA, ANOSIM and SIMPER. Significant spatiotemporal heterogeneity (p < 0.05) was detected in temperature, turbidity, conductivity and total dissolved solids, velocity and depth regimes, channel flow status, sediment deposition, channel alteration and frequency of riffles for the three rivers. Habitat quality ascending hierarchical order was: Umvumvumvu > Murare > Nyanyadzi after Cyclone Idai induced flooding. The highest macroinvertebrate composition diversity was detected in Murare River, the least flood-affected stream. The largest dissimilarity (ANOSIM, 75.3%) was between the Umvumvumvu and Nyanyadzi rivers. Macroinvertebrate community structure differed significantly because of species uniqueness and heterogenous tolerance to changes in environmental conditions after flooding. Flooding intensity and duration induces dissimilar environmental tolerances and persistent macroinvertebrates communities in lotic systems across local catchment scales. Long-term assessment of water quality impairment, resilient functional feeder groups and rifflescale physical habitat structure are imperative for optimised micro-scale conservation of macroinvertebrates in headwater streams prone to flash flooding.

Patterns and Drivers of CO2 and CH4 Fluxes in an Urbanized River Network and Their Response to Restoration

AbstractCarbon evasion from urban river networks becomes increasingly significant as urbanization accelerates. However, there remains a limited understanding of the overall carbon emission impact integrating CO2 and CH4 dynamics, particularly in response to ecological restoration efforts. In this study, we investigated patterns of fluvial CO2 and CH4 diffusive fluxes across an urban river network in Wuxi, China. Our results reveal that water quality variables, especially dissolved oxygen (DO) and phosphorus content, predominantly influence the variability of carbon emissions. These factors exhibit a stronger correlation with CO2 emissions compared to CH4, indicating a net increase in carbon emissions as water quality deteriorates. Seasonally, higher water temperatures, phosphate levels, and lower DO concentrations lead to increased carbon emissions during summer months. Spatially, areas with lower carbon emissions (averaged 86 mmol m−2 d−1 CO2 and 0.13 mmol m−2 d−1 CH4) are primarily situated near the lake and in river sections where significant water quality improvements have been achieved through ecological restoration efforts. Cluster analysis shows that over 60% of high‐carbon emission (averaged 162 mmol m−2 d−1 CO2 and 1.21 mmol m−2 d−1 CH4) sites in the study area have undergone ecological restoration, suggesting potential for further carbon emission reduction through enhanced restoration practices. Our findings underscore the importance of implementing carbon reduction strategies such as nutrient removal and aeration for oxygenation within water ecological restoration initiatives. Effective matching of restoration strategies holds further potential for mitigating carbon emissions from urban river networks.

Wastewater Management as a Part of Solution for Water Resources Problem in Erbil City

In Erbil City, municipal wastewater (MWW) is disposed directly without any treatment processes in the natural environment water body and in some places used for irrigation purposes. Untreated MWW causes problems for the environment, and people's health. Water quantity and quality variation produce complications for water sources in Erbil City, Kurdistan Region-Iraq. There is a lack of performing MWW management fundamentals in the area as well. This research aimed to study the MWW management in Erbil City, presenting water resource difficulties and bringing about suitable solutions for the water resource problems, and also to highlight the risk for the poor Management. MWW Data on Erbil municipal (EMWW) water quality parameters were collected and analyzed for the study period (four months), beginning in October 2022 and ending in March 2023. Twelve EMWW quality parameters were captured and tested in the Laboratory, arranged, and compared with MWW disposal criteria and local standards. The following MWW parameters include pH, electrical conductivity (EC), total dissolved salts (TDS), chloride (CL), total solids (TS), total alkalinity, total acidity, chemical oxygen demand (COD), and colour. The results showed that Erbil's MWW characteristics in terms of temperatures ranged between 11-23 °C and in turn, affected biological species activities. Maximum PH value was 7.7. Total alkalinity found in the range from 140 to 178 mg/l. Chloride concentration results showed for October was 62 mg/l and the maximum value was 80 mg/l in November due to the increment in temperature and evaporation. The range of the COD values of sewage water for five months was 246 -773 mg. Erbil's MWW's moderate to medium strength may be treated and utilized for landscaping, fountains, and irrigation, minimizing the need for fresh water from resources like wells and surface water in the Greater Zab River.

Comparative Analysis of Using Water Quality in Urban Areas of Duhok City, Iraq

Water is sourced from various locations, including lakes, wells, artificial reservoirs, and rivers. Contamination of these sources poses a significant threat to human health, highlighting the need to monitor water quality. This study focuses on evaluating the water quality in the Duhok governorate of the Kurdistan region, Iraq, by analyzing several key physicochemical parameters: turbidity, pH, total dissolved solids (TDS), electrical conductivity (EC), total alkalinity (TAL), total hardness (TH), calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), sulfate (SO₄²⁻), nitrate (NO₃⁻), sodium (Na⁺), and potassium (K⁺). Over a period spanning from January 2019 to December 2021, a total of 1,374 water samples were collected from different sources, including reservoirs, deep wells, springs, the Duhok dam, and the water supply network within the Duhok governorate. The analysis revealed that parameters such as turbidity, pH, TH, Ca²⁺, Mg²⁺, SO₄²⁻, NO₃⁻, and Na⁺ varied significantly across the three years studied. In contrast, other parameters like TDS, EC, TAL, Cl⁻, and K⁺ showed no significant fluctuations. The results also indicated a decrease in most physicochemical parameter values in 2021 compared to 2019 and 2020, with the exception of turbidity. Overall, the majority of water samples were found to be within safe drinking limits. Ongoing monitoring of these water sources is essential to detect any variations in water quality promptly.

Multivariate Analysis of Water Quality in the Seybouse River: Implications for Pollution Management

ABSTRACTHeavy metal contamination in water bodies is a pervasive and persistent environmental challenge in many parts of the world, especially in developing countries. This study investigates the use of multivariate analysis methods for monitoring variations in water quality along a spatial gradient and for the interpretation of pollution levels at different sampling sites. We assessed the water quality of the Seybouse River and identified possible sources of pollution using three complementary multivariate analysis techniques (PCA, NMDS, and K‐means clustering). The results indicate a longitudinal gradient in water quality associated with industrial and agricultural activities in the middle and lower Seybouse River. Physico‐chemical and heavy metal analyses show high water turbidity with elevated concentrations of iron and chromium. We show that the contamination stems from four different sources, which can be categorized into different pollution levels. Our results suggest that complementary multivariate methods are a robust approach to identifying and categorizing significant sources of pollution in rivers, enabling the development of future successful water quality management strategies based on water pollution levels. This study highlights the importance of monitoring water quality and taking effective measures to control and mitigate pollution from various sources to ensure the safety of the environment and human health.

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F-), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

Seasonal Variations in the Thermal Stratification Responses and Water Quality of the Paldang Lake

We evaluated the thermal and chemical stratifications of Paldang Lake using Schmidt’s stability index (SSI) and the chemical stratification index (IC-i) with weekly data from 2013 to 2022. The temporal trends of stratification were analyzed alongside correlations with meteorological, hydrological, and water quality variables. Thermal stratification intensified with rising air temperature and sunshine duration, while hydrological factors like discharge and retention time affected SSI during periods with less than five days of water retention. During summer, fewer occurrences of intense rainfall or early rainfall before August led to stronger stratification. In fall, nutrient influx from external sources during summer stimulated algal growth, increasing Chlorophyll-α (Chl-α) concentrations. Summer rainfall had a significant impact on the strength and duration of stratification in Paldang Lake. Annual rainfall patterns and subsequent changes in discharge were key factors affecting the physical environment of the lake, which in turn determined water quality and the extent of algal blooms. We provide insights into the seasonal stratification and water quality variations in temperate river-type reservoirs like Paldang Lake. SSI and IC-i from this research can be applied to understand stratification and mixing dynamics in other lakes.

Long-term effects of wildfires on river water quality: a comprehensive review of the variability of water quality in South Korea

ABSTRACT After wildfires, the loss of the humus layer leads to increased runoff and ash-related pollutants entering rivers. This study examined the long-term effects of wildfires on river water quality, focusing on 7 years before and after the wildfire events. We statistically analyzed the changes in the water quality of streams surrounding the wildfire area following a major wildfire. We used eight water quality parameters provided by the National Institute of Environmental Research for the analysis. To assess the impact of the wildfires, we employed t-tests and point-biserial correlation analysis to compare the changes in water quality indicators before and after the wildfires. Additionally, an analysis of variance was conducted to evaluate the impact of three wildfires, each occurring in different periods, on the water quality in a single river basin. The results showed increasing trends in hydrogen ion concentration (pH), electrical conductivity, and dissolved oxygen after the wildfire, whereas biochemical oxygen demand, total phosphorus, and total nitrogen exhibited decreasing trends. The impact of wildfires on changes in suspended solids was relatively minimal. It is expected that the results of this study provide valuable insights into developing water quality management and restoration plans following wildfires.

Semi-Supervised Soft Computing for Ammonia Nitrogen Using a Self-Constructing Fuzzy Neural Network with an Active Learning Mechanism

Ammonia nitrogen (NH3-N) is a key water quality variable that is difficult to measure in the water treatment process. Data-driven soft computing is one of the effective approaches to address this issue. Since the detection cost of NH3-N is very expensive, a large number of NH3-N values are missing in the collected water quality dataset, that is, a large number of unlabeled data are obtained. To enhance the prediction accuracy of NH3-N, a semi-supervised soft computing method using a self-constructing fuzzy neural network with an active learning mechanism (SS-SCFNN-ALM) is proposed in this study. In the SS-SCFNN-ALM, firstly, to reduce the computational complexity of active learning, the kernel k-means clustering algorithm is utilized to cluster the labeled and unlabeled data, respectively. Then, the clusters with larger information values are selected from the unlabeled data using a distance metric criterion. Furthermore, to improve the quality of the selected samples, a Gaussian regression model is adopted to eliminate the redundant samples with large similarity from the selected clusters. Finally, the selected unlabeled samples are manually labeled, that is, the NH3-N values are added into the dataset. To realize the semi-supervised soft computing of the NH3-N concentration, the labeled dataset and the manually labeled samples are combined and sent to the developed SCFNN. The experimental results demonstrate that the test root mean square error (RMSE) and test accuracy of the proposed SS-SCFNN-ALM are 0.0638 and 86.31%, respectively, which are better than the SCFNN (without the active learning mechanism), MM, DFNN, SOFNN-HPS, and other comparison algorithms.