Water Quality Variables Research Articles

Climate variability impact on groundwater quality in Small Island Developing States: Mauritius Island as a case study

This study investigates the impact of climate variability on groundwater quality in Mauritius. This is achieved by analyzing the physical and chemical water quality of the five main aquifers over eleven years. Temporal variations in water quality properties were compared to the Standardised Precipitation Index (SPI), Dry Spells, the Standardised Evapotranspiration Index (SPEI), and other climate variables to gain insights into how precipitation controls groundwater quality. The results reveal that the SPI and the SPEI correlate minimally with water quality indicators. Sulphate is the only water quality indicator that showed correlations above 0.4 in aquifers 2 and 3 against a 12-month SPI. Sulphate, alongside chloride, showed what is termed ”notable correlation,” a concept defined in this paper to accommodate correlations that fall above 0.3 when assessed against global climate modes ENSO and AAO, respectively. These results signify that sulphate is the most sensitive water quality indicator to water quantity changes, notwithstanding the modesty of the correlations. Heavy storms occurring during cyclones impact groundwater quality with respect to conductivity, TDS, salinity, and nitrate, although this could not be statistically tested given the lack of water quality indicators collected on the days surrounding the storm. Therefore, the conclusion is made based on one storm event. The study revealed that individual correlations between climate indices and water quality variables are present but weak. However, the long-term trend in water quality is visible.

Exploring the effects of climate change and urban policies on lake water quality using remote sensing and explainable artificial intelligence

Climate change and urban policies significantly impact lake water quality, especially in rapidly developing regions. Existing impact assessments are limited by the duration and resolution of water quality data, while long-term impacts and contributions of climate and socioeconomic drivers have rarely been quantified. In this study, we utilized remote sensing imagery to obtain high spatiotemporal resolution data and Explainable Artificial Intelligence (XAI) to reveal how the comprehensive impacts vary over time. We obtained Trophic State Index (TSI) data between 2000 and 2022 from remote sensing imagery. XAI was employed to elucidate the intricate connections between TSI and the multifaceted factors that encompass climate and socioeconomic dynamics. Six typical lakes in East China were selected for this study. Results showed that the TSI of these lakes had generally decreased over the past two decades, being typically higher in the littoral zones compared to the center. Moreover, higher temperatures increased lake TSI. Meanwhile, higher rainfall increased TSI through increased lake nutrient inputs from surrounding watersheds where agricultural and industrial activities are intensive. It was also found that economic development impacts on lake TSI shifted around 2013. Economic development caused severe negative effects on lake water quality before 2013, while this phenomenon decreased thereafter. This is attributed to the implementation of a series of green transformation policies in the region. This study demonstrates the long-term contribution of climate change and urban policies in driving lake water quality variations, and provides implications for policy makers to establish positive economic, social, and environmental linkages.

Algal diversity and its relationship with seasonal variation of water quality in Gajedi Lake, Rupandehi District (Nepal)

The current study focuses on the algal diversity of Gajedi Lake in different seasons in connection to the lake's water quality indicators. Algae and water samples were collected from eight peripheral sites of the lake in summer, rainy, and winter seasons in 2021. The water temperature, conductivity, total dissolved solids, hardness, alkalinity, phosphorus, free CO2, and dissolved oxygen were recorded as high during the summer season while water pH was high during the rainy season. Altogether 93 species of algae were reported under 52 genera, 30 families, 7 classes and 6 phyla in this study. The largest phylum was Bacillariophyta (44.08%) followed by Chlorophyta (17.20%), Euglenozoa (10.97%), Cyanobacteria (12.90%), Charophyta (10.75%) and Miozoa (1.07%). The Shannon-Weiner diversity index indicated high abundance during the summer season, with more evenly distributed algal species. More algal species were recorded in the winter season. Trachelomonas, Navicula, and Nitzschia were dominant genera during summer; Anagostidinema, Desmodesmus, and Pinnularia were dominant during rainy; and Trachelomonas, Tetradesmus, and Gomphonema were dominant during winter. The redundancy analysis ordination showed that the algal composition varied along with the seasonal changes in physico-chemical parameters. Temperature, pH, and conductivity were thus identified as significant factors influencing species richness.

Assessing residual chlorine levels for water security at the Lower Usuma Dam treatment plant: Implications for Sustainable Development Goal 6

Despite advancements in water treatment, the degradation of water quality during distribution remains under-researched in the Federal Capital Territory (FCT), Nigeria. This study addresses this gap by evaluating the physicochemical and microbial quality of the water distribution system, aligning with Sustainable Development Goal 6 (SDG 6), which focuses on water and sanitation management. The effectiveness of water treatment and distribution processes was assessed, identifying variations in water quality across 11 distribution points. Although most physicochemical parameters met standards, manganese levels exceeded WHO guidelines at 10 locations, and low residual chlorine was linked to microbial contamination. These findings underscore the need for enhanced monitoring and treatment, providing recommendations to improve water quality management and protect public health.

Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing

ABSTRACT Machine learning models for water quality prediction often face challenges due to insufficient data and uneven spatial-temporal distributions. To address these issues, we introduce a framework combining machine learning, numerical modeling, and remote sensing imagery to predict coastal water turbidity, a key water quality proxy. This approach was tested in the Great Lakes region, specifically Cleveland Harbor, Lake Erie. We trained models using observed data and synthetic data from 3D numerical models and tested them against in situ and remote sensing data from PlanetLabs' Dove satellites. High-resolution (HR) data improved prediction accuracy, with RMSE values of 0.154 and 0.146 log10(FNU) and R2 values of 0.92 and 0.93 for validation and test datasets, respectively. Our study highlights the importance of unified turbidity measures for data comparability. The machine learning model demonstrated skill in predicting turbidity through transfer learning, indicating applicability in diverse, data-scarce regions. This approach can enhance decision support systems for coastal environments by providing accurate, timely predictions of water quality variables. Our methodology offers robust strategies for turbidity and water quality monitoring and has potential for improving input data quality for numerical models and developing predictive models from remote sensing data.

Growth of Vaname Shrimp (Litopenaeus vannamei) Infested with Extracts of Ectoparasites Chlorella vulgaris as an Immunostimulant

Abstract Vannamei shrimp (Litopenaeus vannamei) has a high market demand, encouraging production to minimize stress, water quality degradation and disease attack. Ectoparasite attacks come from the genera Epistylis, Zoothamnium, and Vorticella. Body defense through immunostimulants from Chlorella vulgaris extract which has bioactive compounds, vitamins and proteins. The purpose of the study was to determine the effect of giving Chlorella vulgaris extract with different doses and length of maintenance, as well as the interaction of Chlorella vulgaris as an immunostimulant on the growth of vaname shrimp infested with ectoparasites. The research method used a complete randomized experimental design with a factorial pattern consisting of factor A, the dose of Chlorella vulgaris extract, namely without giving Chlorella vulgaris extract (A0), 400 mg/kg (A1), 800 mg/kg (A2), 1200 mg/kg (A3) and factor B, the length of shrimp rearing, namely 0 days (B1), 7 days (B2), 14 days (B3), 21 days (B4), 28 days (B5). This study uses three variables, namely independent variables administration of Chlorella vulgaris Extract and length of maintenance, bound variables Survival (SR), Specific Growth Rate (SGR) and Absolute Growth (GR) and Absolute Growth (GR), and control variables water quality (temperature, pH, dissolved oxygen, salinity, and ammonia). The results of the study at dose A2 with length of rearing B5 gave a real effect (p<0.05) on the growth of vaname shrimp infested with ectoparasites, namely survival (50%), specific growth rate (4,17%), and absolute growth (weight (1,39 grams), length (1,10 cm)).

Hydro-chemical characterization and irrigation suitability assessment of a tropical decaying river in India

Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station’s data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+–HCO3− types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3− + CO32−) outperform strong acids (Cl− + SO42−). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p < 0.05). The results also revealed that river water for irrigation during monsoon is suitable compared to the pre-monsoon season. Anthropogenic interventions including riverbed agriculture, and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river, which needs substantial attention from the various stakeholders in a participatory, and sustainable manner.

Seasonal natural organic matter removal and implications for disinfection by-product formation at the Koka water treatment plant: Upper Awash, Ethiopia

ABSTRACT This study evaluated the seasonal performance of the Koka water treatment plant in removing natural organic matter (NOM) and the implications for disinfection by-product (DBP) formation potential. Raw and treated water samples were collected during the dry and wet seasons and analyzed using physicochemical parameters and fluorescence spectroscopy. The results revealed significant seasonal variations in raw water (RW) quality, with higher turbidity, pH, temperature, conductivity, total organic carbon (TOC), dissolved organic carbon (DOC), and UV254 absorbance during both seasons and across treatment processes. The NOM removal efficiency of the treatment plant was poor, with mean TOC removal of 46 and 43% and DOC removal of 15.8 and 15.2% during dry and wet seasons, respectively. The sedimentation unit demonstrated negative TOC removal, indicating NOM accumulation likely due to biochemical reactions in the unit. Fluorescence analysis and the correlation between specific ultraviolet absorbance (SUVA) and DBP formation potential suggest a higher risk of DBP formation in chlorinated drinking water. These findings highlight the influence of seasonal variations, RW quality, and the treatment process dynamics on the plant's performance in removing NOM. There is a need to implement adaptable strategies to enhance NOM removal, accounting for seasonal fluctuations in RW quality.

A high resolution hydrodynamic-biogeochemical-oyster-filtration model predicts that the presence of oysters (Crassostrea virginica) can improve, or reduce, water quality depending upon oyster abundance and location

The eastern oyster (Crassostrea virginica) provides numerous ecosystem services such as building reef habitat, clarifying the water by filtering seston, and reducing excess nitrogen when their biodeposits are denitrified. Some of these ecosystem services have been extensively studied using two-dimensional and three-dimensional (3D) models. Yet, the relationship between oyster abundance, their filtration and biodeposition rates, and associated water quality metrics has not been estimated with high-resolution models that include biodeposit resuspension as well as simulation of 3D processes in the sub-tributaries where oysters are abundant. To undertake these estimates, a 3D Regional Ocean Modeling System (ROMS) framework, comprised of a coupled hydrodynamic-water quality model with an oyster filtration and biodeposition sub-model, was implemented over a fine-resolution model grid (120–150 m) of the Choptank River on the eastern shore of Chesapeake Bay, U.S.A. After validation with data from 20 cruises between May and September 2010, the model was used to predict seven variables associated with water quality in simulations with zero, recent, and 50x recent abundances of oysters. Results indicated that improvement in the seven water quality variables differed in response to the abundance of oysters and between regions in the Choptank River. In line with expectations, the water quality metrics improved with increasing oyster abundance in the shallow and retentive sub-tributaries of Broad and Harris Creeks. In contrast, in the deeper and more flushed mainstem of the Choptank River, some water quality metrics deteriorated when recent abundances of oysters were added to the model compared to the simulation with zero oysters and the same metrics improved when oyster abundances were increased by 50x. Overall, model results indicated that the many complex physical and biogeochemical processes that influence the effect of oysters on water quality resulted in differing responses to oysters across different systems. In addition, this study introduces a high-resolution, predictive tool that could be used to estimate the number of oysters needed to meet water quality thresholds in specific systems in support of ecosystem management.

Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems

This study introduces a novel method for assessing water quality, employing a cutting-edge sensor system integrated with artificial intelligence (AI) technologies. Addressing the global challenge of water scarcity and pollution, the research focuses on the innovative use of spectroscopic analysis for real-time water quality monitoring. The study evaluates the effectiveness of this system in distinguishing between clean, contaminated, and UV-disinfected water samples, highlighting its precision in detecting variations in water quality. Central to the research is the deployment of advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, to process and classify spectral data. These models demonstrate remarkable accuracy in real-time classification, underscoring the synergy between AI and environmental science in addressing critical public health issues. Significantly, the study showcases the potential of UV disinfection in water treatment, as evidenced by the spectral changes observed in disinfected water samples. This aspect of the research emphasizes the role of spectral analysis in verifying the efficacy of water treatment processes. Overall, this study paves the way for more sophisticated and accessible water quality monitoring systems, offering a promising solution to one of the most pressing environmental challenges. The integration of AI and spectral analysis in this research offers a breakthrough in ensuring a safe water supply and effective water resource management. This study utilizes advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, for water quality assessment. These models process and classify spectral data with high precision, highlighting variations in water quality.

Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators—permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)—and six air pollution indicators—O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)—using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann–Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

Components of dissolved organic carbon in relation to environmental factors in lakes along an altitudinal gradient in central China.

Dissolved organic carbon (DOC) is an important organic substance that affects biogeochemical processes and underwater light conditions in aquatic ecosystems. To explore altitudinal variations in DOC components and water quality, 66 surface water samples were collected along an altitudinal gradient (10-1857m) in three regions of central China, including a montane region (Enshi Prefecture) and two floodplain regions (Dongting Lake Basin and Wuhan City). DOC components were measured using a three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy, in conjunction with fluorescence regional integration (FRI) and fluorescence indices. Generally, lakes at high elevations (Enshi Prefecture) had higher DOC concentrations (a mean value of 6.43mgL-1) than floodplain lakes in Dongting Basin (4.51mgL-1) and Wuhan City (3.89mgL-1). Fluorescence index (FI, a range of 1.37-1.92) and biological index (BIX, a range of 0.47-0.74) indicated that DOC mainly originated from allochthonous sources in the three regions. Enshi Prefecture had relatively lower FI and BIX values, suggestive of a larger contribution of allochthonous carbon in this montane region in comparison with the floodplain regions. Humic-like substances were more abundant than other four fluorescent components, including tyrosine-like, tryptophan-like, fulvic acid-like, microbial-like substances. Spearman rank correlation analyses showed that tryptophane-like substances were positively correlated with K+, Mg2+, and Na+, while humic-like substances were negatively correlated with these cations. Taken together, the results can improve our understanding on altitudinal variations in DOC components and potential driving forces.

Hybrid modelling of nitrogen removal by biofiltration using high-frequent operational data

ABSTRACT In this research, a parallel hybrid model is presented for the simulation of nitrogen removal by submerged biofiltration of a very large-size wastewater treatment plant. This hybrid model consists of a machine learning model and a mechanistic model that are combined to produce accurate predictions of water quality variables. The models are calibrated/validated using detailed and quality-controlled operational data collected over a period of 3.5 months in 2020. The mechanistic model applied is a modified activated sludge model that describes the biological, physical and chemical processes taking place in a biofilm reactor based on the domain knowledge of these processes. A three-layer feed-forward artificial neural network with a rectified linear activation function that aims to reduce the mechanistic model's residual error and then correct its output. The results show how the hybrid model outperforms and significantly reduces the size of the mechanistic model's prediction errors of the effluent nitrate concentration from a relative mean error of 12% for the mechanistic model to 2% for the hybrid model during training. The error on nitrate simulations increases to 8% during hybrid model testing, still significantly lower than the error of the mechanistic model. These results support future operational applications of hybrid biofilm models.

Assessment of the ecological status of an ancient reservoir using macroinvertebrate assemblage and water quality parameters

BackgroundAtori Reservoir, located in the heart of southwestern Nigeria, is a tribute to the region's historical significance and natural splendor. Its establishment as a reservoir in 1935 marked a water resource management watershed in the region, transforming Atori into one of Nigeria’s oldest and most important waterbodies. Despite its ancient age and indisputable importance, the exact ecological state of Atori Reservoir has been shrouded in mystery for decades. This study was necessitated by a paucity of information on the ecological status of Atori Reservoir.ResultsThe study identified 953 macroinvertebrates belonging to four classes, distributed across nine orders and nineteen families within Atori Reservoir. Among these, the class Insecta exhibited the highest diversity, while Melanoides tuberculata, a member of the Mollusca class, emerged as the dominant species. Despite the diverse macroinvertebrate community, the physicochemical parameters of the water raised concerns. Elevated values of total dissolved solids and conductivity indicated poor water quality, which was further reflected in the predominance of pollution-tolerant species and the absence of pollution-sensitive ones. Canonical correspondence analysis highlighted potential correlations between macroinvertebrates and water quality variables, yet statistical significance was lacking, as demonstrated by the Monte Carlo permutation test.ConclusionThis study sheds light on the ecological state of Atori Reservoir, revealing a diverse macroinvertebrate community but highlighting concerns regarding water quality. The study also emphasizes the pressing need for improved management practices to safeguard the ecological health of Atori Reservoir, given the critical role it plays in the region's ecosystem and local communities.

Simulation of Maize Growth Under the Applications of Brackish Water in Northwest China

The objective of this study is to assess the suitability of the AquaCrop model for growing maize using brackish water irrigation in Northwest China. Additionally, this study aims to examine how maize utilizes water in various soil layers when irrigated with varying water qualities. The AquaCrop model was calibrated and verified using experimental data from the years 2022 and 2023 in this research. (1) The findings indicated that the AquaCrop model effectively simulated the canopy cover, biomass, and yield of maize when irrigated with brackish water. The validation year’s R2, MAPE, and RMSE values for canopy cover, biomass, and yield of maize were 0.95, 5.36%, and 4.77%, respectively. For biomass, the R2, MAPE, and RMSE values were 0.91, 16.61%, and 2.12 t·hm−2, respectively. For yield, the R2, MAPE, and RMSE values were 0.84, 3.62%, and 0.42 t·hm−2, respectively. (2) Irrigation with water of high mineral content, measured at 1.6 ds/m, as well as with fresh water over the whole reproductive period, resulted in an increased reliance on groundwater for maize cultivation. There was no notable disparity in the usage of various soil layers between the irrigation with alternating freshwater and brackish water. (3) The AquaCrop model simulated the effects of seven different irrigation water quality treatments. It was shown that using water with mineralization levels of 0.5 and 0.8 ds/m resulted in decreased freshwater use without causing a substantial decrease in maize yield and biomass.

An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China

Intensifying anthropogenic disturbances have caused water pollution in China in recent decades. China has a vast territory with diverse climate conditions, land use types, and human activities, leading to significant water quality variability. However, few studies have investigated nationwide spatiotemporal patterns of key water quality parameters. In this study, we analyze monthly water quality observations from 3647 gauge stations to understand how water quality changes over time and space in China. We group the stations by water resource regions and adopt Python and SPSS to analyze the spatiotemporal variability and intercorrelations of eight water quality parameters. Results indicate that the concentrations of biochemical oxygen demand of 5 days (BOD5), chemical oxygen demand (COD), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) show similar spatial patterns, with higher concentrations in the northern parts than the southern regions of China. The concentrations of COD and TP are higher in the rainy season than in the dry season, while DO, NH3-N, and TN show the opposite seasonal patterns. Strong positive correlations were found between BOD and COD, NH3-N and TP. The annual cumulative distribution figures demonstrate that all parameters showed slightly lower concentrations in 2022 and 2023 than in 2021, except for DO and TN. The TN/TP ratios across different water resource regions in China are significantly higher than 16, indicating that phosphorus is the limiting factor of eutrophication. This investigation provides a comprehensive understanding of the spatiotemporal variability of water quality parameters across China. The results of this study are highly valuable for investigating mechanisms regulating water quality across large spatial scales, thus providing valuable implications for improving water quality and mitigating water pollution.

Spatial-temporal distribution characteristics of surface water pollutants and their potential sources in Ngari, China.

The Ngari region has many important rivers and is critical to water resource security and water resource continuity in China and even in adjoining Asian countries. However, the spatial distribution and monthly variation in local water quality have been poorly understood until recently. In this study, the spatial-temporal variations of 12 water quality parameters, including pH, dissolved oxygen (DO), permanganate index (IMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NNH3), total nitrogen (Ntotal), total phosphorus (Ptotal), copper (Cu), fluoride (F), arsenic (As) and cadmium (Cd), were determined from samples collected monthly at 22 water cross-sectional sites in the Ngari region in 2020. The surface water pollution in the southern Ngari region was the most serious, and the water pollution level in winter was higher than that in the other seasons. As (0.0781 ~ 0.6154 mg/L) and F (1.05 ~ 4.64 mg/L) were the main exceedance factors derived from the recharge of high arsenic and fluoride geothermal water and weathering of As and F-bearing minerals. The hazard quotient and carcinogenic risk for As and F at the five contaminated sampling sites indicated potential health risks and even carcinogenicity to local populations. The hydrochemistry types of the lakes and rivers in the Ngari region were mainly chloride water and carbonate water. The results from this study can provide a scientific basis for the prevention and control of surface water pollution in the Ngari region and contribute to subsequent research on the ecology of water bodies.

Spatiotemporal and vertical variability of water quality in lentic small water bodies: implications of varying rainfall and land use conditions.

Lentic small water bodies (LSWBs) deteriorate owing to anthropogenic activities, such as untreated domestic and agricultural waste disposal. Moreover, different turnover mechanisms occur during different seasons, contributing to nutrient enrichment and consequent degradation of LSWBs. However, understanding their spatial, temporal, and vertical variations during different seasons is understudied. In addition, studies on the variation in water quality under varying rainfall and land-use conditions are limited. Therefore, in this study, three LSWBs located in Northern India were studied during the pre-monsoon and monsoon seasons (December 2022 to October 2023). Total nitrogen (TN), chlorophyll-a (Chl-a), total phosphorus (TP), temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), chemical oxygen demand (COD),secchi disk depth (SDD), and water level (WL) were measured monthly. Sentinel-2 and CHIRPS pentad data were used for land use, land cover classification, and rainfall analysis. The spatial analysis indicates that the seasonal shift affects the water quality distribution, especially near the inlets and at the edges. The overall concentrations of TN and TP decreased during the monsoon season; however, they increased significantly at the inlets of the LSWBs. On the other hand, the Chl-a concentration shifted towards the edges due to the inflow during the monsoon. Temporal analysis also suggests that the arrival of the monsoon lowers pH, DO, and TDS. However, the concentrations of TN and TP increased because of agricultural runoff. Chl-a and COD show distinct variations due to the individual LSWBs' local conditions. Vertical variability analysis demonstrated pH, temperature, and TN stratification during the pre-monsoon period. However, during the monsoon, stratification is less significant due to intermixing. Redundancy analysis (RDA) showed that land use and rainfall patterns affected the water quality of LSWB 1, 2, and 3 by 53.49%, 81.62%, and 92.64%, respectively. This shows that land use, land cover, and rainfall changes affect the water quality of LSWBs. This study highlights the negative impact of runoff from agricultural land use as the main factor responsible for increased nutrient levels in the LSWBs.

Assessing the Suitability of CCME WQI as a Groundwater Quality Monitoring Tool: An Environmental Ergonomics Case Analysis

This study was conducted with the aim of protecting groundwater, which plays a crucial role in ensuring food quality in the market, preserving public health, and safeguarding the ecosystem, as many regions rely on clean natural groundwater for their population’s survival. The objective of this study was to use the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) for groundwater at 12 stations in the Okhla Industrial Area, Nangloi, and Karol Bagh in the Delhi Region. CCME WQI is an effective tool for assessing groundwater quality and communicating water conditions to various users. The research methodology involved fieldwork from June to October 2020 for three different periods in the year: pre-monsoon, monsoon, and post-monsoon, to observe variations in water quality and differences in various physicochemical properties of water. The CCME WQI was applied using sixteen water quality parameters, fourteen of which were physicochemical parameters and two of which were microbiological parameters. Among the physicochemical parameters were color, odor, pH, turbidity, nitrate, total hardness, iron, chloride, fluoride, total dissolved solids, calcium, magnesium, sulfate, and alkalinity, while the microbiological parameters included the total coliform and Escherichia coli counts. Based on the results obtained from the water quality index, station A9 scored between 0 and 44, indicating the lowest water quality index due to wastewater discharges and industrial contamination. The water quality at other stations also requires attention to achieve excellent ratings. The study concludes that serious measures should be taken for proper management of the area to protect the population from hazardous diseases. The research results show that stations 1, 2, and 10 were rated as excellent, station 12 as good, stations 4, 5, and 8 as moderate, stations 3, 6, and 11 as marginal, and station 9 as the poorest in terms of water quality in the year 2020 during the pre-monsoon, monsoon, and post-monsoon periods. To improve the parameters and groundwater quality, it would be necessary to reduce the impact of industry, anthropogenic–geogenic activities, and domestic activities.

Do gravel highways affect water quality and invertebrate communities in Arctic lakes?

ABSTRACT Gravel roads are a common feature in developed areas of Canada’s Arctic. These roads can be a source of calcareous dust that drifts to roadside lakes, causing significant changes in conductivity, calcium, and pH levels. In this study, we examined if road proximity was associated with differences in water quality and invertebrate communities in lakes along the Dempster and Inuvik-Tuktoyaktuk Highways in the Northwest Territories, Canada. We collected biological and water quality data from 18 lakes selected using a stratified random sampling design, with distance from the road (0–300 m, 300–600 m, and >600 m) and region of study (boreal forest, tundra) as the two factors. We hypothesized that lakes closer to the road would exhibit differences in water quality and invertebrate communities associated with road dust pollution and other stressors caused by roads. We found no clear differences in water quality or invertebrate communities among lakes based on distance from the highways. In addition, while there were differences between regions, these did not appear to be related to the effects of the roadways. Our results suggest that variability in lake morphometry and water quality in this region might be more important than the influence of roads.