Water Quality Variables Research Articles

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.

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.

Exploring spatial and seasonal water quality variations in Kelani River, Sri Lanka: a latent variable approach.

Water quality degradation poses a significant challenge globally, especially in developing nations like Sri Lanka. Extensive monitoring programs designed to address escalating river pollution collect multiple water quality parameters over extended periods and varied locations. However, the sheer volume of data can be overwhelming, making it difficult to process effectively and interpret accurately using conventional methods. In this study, latent variable (LV) and unsupervised machine learning techniques were used to investigate spatial and seasonal variations of surface water quality for 17 parameters across 17 locations along the Kelani River, Sri Lanka, using monthly water quality parameters from 2016 to 2020. Pearson's correlation matrix identified 10 parameters significantly affecting water quality variations and factor analysis (FA) generated five LVs, accounting for 77% of the total variance in the dataset. The identified LVs showed multiple methods of river pollution.Hierarchical clustering analysis and self-organizing mapping methods clustered stations in a closely analogous manner. Stations near industrial zones and the river mouth showed higher water quality variance, often exceeding national guidelines. Correlation testing revealed strong relationships between water quality and catchment hydrometeorological variations during monsoonal seasons. Spatial analyses showed increased LV variance in the Lower Kelani River Basin, indicating higher pollutant levels in different seasons. Industrial effluents (LV-2 and LV-4) and domestic and municipal sewage (LV-3 and LV-5) exhibit greater seasonal fluctuations. The results showed that the proposed LV approach has the potential to assist authorities in addressing water pollution amidst the complexity of multiple water quality parameters.

Statistical assessment for reusing permeate from dewatering water treatment sludge process: more sustainability in environmental sanitation.

Sludge from water treatment plants (WTPs) is usually processed by physicochemical clarification followed by thickening, which results in the production of an effluent from dewatering/drying sludge processes that can potentially impact the environment. This paper assessed the viability of employing sludge dewatering water from a water treatment sludge plant (WTSP) in São Paulo State, Brazil, for reuse purposes. Water quality variables were monitored in the effluent and receiving stream. The data were analyzed by paired samples Student t-test (parametric significance test), paired samples Wilcoxon signed rank test (non-parametric significance test), and principal component analysis (multivariate analysis). Despite the distribution of data, typically not Gaussian, both Student and Wilcoxon methods agreed in 9 out of 10 studied parameters regarding the influence of WTSP discharge on waterbody; only for total manganese the Wilcoxon approach provided better fit than Student. Principal component analysis helped to evince correlations among all variables. Results provided useful information for understanding the vocation of WTSP effluent for reuse. For direct non-potable reuse, recirculating the final effluent back to the WTP for two months saved 92,000 m3 of water, the volume of drinking water demanded by the city (n = 292,000 inhabitants) in approximately 30h.

Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China

Field surveys and sample analyses were conducted from January 2018 to December 2019 on Daye Lake, Cihu Lake, Baoan Lake, and Xiandao Lake to understand the water quality characteristics of typical lakes in southeast Hubei. A fuzzy comprehensive evaluation was conducted and the comprehensive trophic level index was applied to evaluate the lakes’ water quality. The results showed differences in the regional, spatial, and temporal distributions of physical and chemical indicators in typical lakes in southeast Hubei. The fuzzy comprehensive evaluation showed that the water quality levels in Daye, Cihu, Baoan, and Xiandao Lakes for 2018 and 2019 were IV, IV, III, and II and V, IV, III, and II, respectively, with seasonal variations in water quality occurring during the year. The trophic level index results showed that Cihu Lake was mildly eutrophic in 2018 and moderately eutrophic in 2019, and Daye, Baoan, and Xiandao Lakes were mildly eutrophic, mildly eutrophic, and mesotrophic, respectively. Lake water quality was influenced by land use types, landscape configuration, inflowing rivers, precipitation, and interactions between land use and seasons. This study helps us to understand the trend and causes of lake pollution in Southeast Hubei, which is conducive to watershed management and the control of water quality deterioration, and also has an important role in regulating the sustainable development of industry and agriculture in the watershed.

Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management

River water quality continues to deteriorate under the coupled effects of climate change and human activities. Machine learning (ML) is a promising approach for analyzing water quality. Nevertheless, the spatiotemporal dynamics of river water quality and their potential mechanisms in changing environments remain incomprehensively understood through available ML-based researches. Here, we developed a ML-based framework integrating a self-organizing map (SOM) model with a random forest (RF) model. This framework was applied to simultaneously investigate the spatiotemporal patterns and potential drivers of river permanganate (CODMn), ammonia nitrogen (NH3-N), and total phosphorus (TP) dynamics across 34 sites from 2010 to 2020 in a coastal city threatened by deteriorating water environment in southeastern China. The sites were divided into two clusters in the spatial context with different water quality conditions. The year of 2015 for NH3-N and 2018 for CODMn and TP were identified as the key turning points of water quality variations. Features including sewage discharge, population dynamics, percentage of cultivated land, and fertilizer application contributed greatly to water quality deterioration. The increase in forest vegetation reflected by percentage of forest and leaf area index may improve water quality. The ML-based modeling framework demonstrated a promising way to address the spatiotemporal dynamics of river water quality, and provided insights for water management in a coastal city with intensifying human-nature interactions.

Aquatic insect biodiversity, water quality variables, and microplastics in the living weir freshwater ecosystem

Most Thai rivers have not yet reached "good" ecological status. Furthermore, the scientific community is deeply concerned about the existence and abundance of microplastics (MPs) in freshwater. The live weirs' river health status was assessed using water quality and a variety of aquatic insect species. The research was conducted along Khlong-La, which was compared in the absence (R1, R2) and presence of living weirs (LWs) (M71, M62, M4, and M2). In 2023, aquatic insects and water samples were gathered during two months: January (rainy season) and July (dry season). The results revealed that the six sampling sites had similar physical and chemical properties, with the exception of total dissolved solids, which varied significantly. A total of 658 aquatic insects from 25 families and 6 orders were identified. The Hydropsychidae dominated the aquatic insect fauna at the LWs locations, followed by Chironomidae, Naucoridae, and Caenidae. Ten genera were discovered at the reference site (R1), whereas 16, 21, 24, and 19 were discovered at LWs (M71, M62, M4, M2), and 16 at the reference site (R2). The results of CCA ordination revealed that the most important factors influencing aquatic insect diversity were dissolved oxygen, water temperature, pH, electrical conductivity, and ammonia nitrogen. This study suggests that because of their significant responses to environmental factors, the species richness of aquatic insects may indicate the conservation value of habitats. The BMWPTHAI score was 41, 51, 57, 58, 73, 54, and the ASPTTHAI score was 5.12, 5.10, 5.18, 5.27, 4.86, and 5.40, respectively. Shannon-Weiner diversity index (Hʼ) values were 2.059, 2.461, 2.839, 2.777, and 2.587, respectively. The results showed that the water quality was average. The physicochemical data, as well as the BMWP and ASPT, revealed that the water at the living well was slightly polluted. Furthermore, larger MPs (&gt;500 µm) were found in the largest concentrations, in the shape of fibers, and primarily in the color violet.

Integrated environmental and social assessment of alum-contaminated water in An Giang province

ABSTRACT This study investigates alum-contaminated water in An Giang province within the Mekong Delta, emphasizing methodological approaches to assess its distribution and impacts. The research conducted from March to May 2021 involved surveys of residents, public sector employees, and provincial managers, complemented by expert interviews and extensive fieldwork. Surface water samples were collected during both rainy and dry seasons across three districts, with parameters such as turbidity, pH, and temperature measured onsite, and aluminum (Al), iron (Fe), and copper (Cu) concentrations analyzed in the laboratory using atomic absorption spectrometry. The data were further processed using QGIS software to create comprehensive maps illustrating seasonal variations in water quality. The findings revealed significant spatial and temporal variations in water quality, with pH levels fluctuating markedly between the dry and rainy seasons. Household surveys indicated a lack of awareness regarding effective water treatment methods, with most respondents relying on local authorities for solutions. The study highlights the need for community education and participation in addressing alum contamination, offering insights into the environmental and public health implications of water quality in the region. This research contributes valuable knowledge to the ongoing efforts in environmental conservation and water quality management in the Mekong Delta.

Trait-specific groups of aquatic macrophytes respond differently to eutrophication of unshaded lowland streams

Understanding eutrophication effects on stream macrophytes is key to meaningful environmental management and governance. However, the response of macrophyte communities to eutrophication is complex and often the effect of eutrophication is obscured by other factors related to habitat variability including flow regime and light availability.In this study macrophyte community composition and abundance was quantified along a eutrophication gradient in 30 lowland streams. Slow flowing, medium sized streams with minimal shading were selected, to focus on examining water quality effects. The internal nutrient status of the plants and the bioavailable nutrient fraction of the sediment was measured and a national database of modelled stream descriptors was used to obtain information about land use and water quality variables. Statistical relationships between macrophyte community composition and clogginess and nutrient, underwater light, and carbon availability and flow disturbance were examined along the eutrophication gradient.Macrophytes with different growth strategies were related to different aspects of eutrophication and non-native taxa were found to dominate eutrophic streams.There was no relationship between nutrient concentrations and nutrient content of the macrophytes but sediment P was correlated with P concentrations in streams.Our results showed that 1) non-native species were common and clogginess was high in unshaded lowland streams with low water clarity and high nutrient and bicarbonate availability; 2) emergent species were associated with high nitrate availability and sediment phosphorus and low disturbance; 3) submerged species prevailed where water column total phosphorus and bicarbonate availability were high; and 4) nutrient concentrations that limit the development of clogginess are expected to be low where bicarbonate is high and flow or other physical disturbance is low. Our findings improve understanding of how stream macrophyte communities respond to changes in water quality related to eutrophication and anthropogenic changes in catchments and will contribute to informed management of macrophytes in streams.