Water Quality Analysis Research Articles

Water quality level estimation using IoT sensors and probabilistic machine learning model

ABSTRACT Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensembles the random forest model and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%.

Research, assessing and water quality change trends in the Holocene and Pleistocene beds to meet water supply requirements for the coastal area of Binh Thuan province, Vietnam

The study conducted water sampling at 30 well locations in 3 aquifers with different water depths (X15 m, X24 m, X36 m) in the Holocene and Pleistocene aquifers in the coastal area of Binh Thuan province, Vietnam. Water quality index calculation methods, correlation analysis, and principal component analysis were applied. The results showed that the Groundwater Quality Index fluctuated at “Excellent”, accounting for 18.61 %; “Good” accounts for 38.05 %; “Poor” accounts for 20.55 %; the remaining “Very Poor” and “Inappropriate” account for 22.79 %. The most “inappropriateˮ part is in Ham Thuan, Phan Thiet. Some well sites are not suitable for water supply. In the coastal area of Phan Thiet, the average chloride content during the year is higher than the allowed standard. During 3 years of monitoring, in the dry season, the highest chloride content was 478 mg.L–1 at sites GWBT16, GWBT20, and 521 mg.L–1 at GWBT24. The results of the principal component correlation analysis of water quality in the X15 m aquifer show a tendency to be more severely affected by high tides during the rainy season.

Review paper on applications of the HEC-RAS model for flooding, agriculture, and water quality simulation

ABSTRACT The Hydrologic Engineering Center (HEC) of the United States Army Corps of Engineers (USACE) has developed the HEC-River Analysis System (RAS) hydraulic channel flow model as part of its portfolio of hydrologic and hydraulic modeling tools. HEC-RAS is a new version with capabilities to model flooding, modeling for agriculture production computations for water quality, and many more related to hydraulic modeling. The analysis components of the HEC-RAS system include one-dimensional steady flow water surface profile computations, one-dimensional and or two-dimensional unsteady flow simulation, one-dimensional and two-dimensional computations of quasi-unsteady or fully unsteady flow movable boundary sediment transport, and one-dimensional water quality analysis. The main objective of this paper is to review the use of HEC-RAS on flooding, agriculture, and water quality. The three primary components of the data input are plan data, geometry data, and flow data. Three of these scenarios applied to HEC-RAS and were proven by previous reviewers.

Bongso River Water Quality Status Due to Traditional Oil Mining Activities Using the Pollution Index Method

Traditional petroleum mining activities in 137 old wells in Wonocolo Village, Bojonegoro Regency Council District, East Java, have a positive impact on the local community's economy, but also have a negative impact on the environment in the form of pollution of surface water. The study aims to determine the quality of the Bongso River in the traditional Wonocolo petroleum mining area based on physical and chemical parameters (temperature, pH, DO, TSS, TDS, COD, phenol, and nitrate), as well as analyze the level of pollution in the river using the Pollution Index method. The results of the Bongso River water quality analysis contained parameters that exceeded quality standards, namely, SB 1 station with COD levels of 47.1 mg / L and Phenol 0.196 mg / L. SB 2 station with TDS levels of 1010 mg / L, TSS 93.60 mg / L, COD 94.0 mg / L and phenol 0.552 mg / L. SB 3 station TDS levels 1190 mg / L, TSS 1778 mg/L, COD 84.7 mg/L, and phenol 0.747. Station SB 4 levels TDS worth 1450 mg / L, TSS 1019 mg / L, COD 28.2 mg / L, phenol 0.632 mg / L and DO levels do not meet the minimum quality standards of 3.4 mg / L. Station SB 5 levels TDS 1310 mg / L, COD 103.5 mg / L and phenol 0.364 mg / L. Station SB 6 levels TDS 1400 mg / L, COD 65.9 mg/L and phenol 0.585 mg/L. Station SB 7 levels TDS 1230 mg/L, TSS 54.80 mg/L and phenol 0.556 mg/L. Salinity of the Bongso River increased from upstream (SB1) to middle (SB2); 0 ppt to 4 ppt (rainy season). Water quality status using the IP method shows that SB 1 and SB 5 stations are categorized as Lightly Polluted. Meanwhile, SB 2, SB 3, SB 4, SB 6, and SB 7 stations are categorized as Moderately Polluted.

A fused convolutional transformer for voltammetric electronic tongue analysis tasks

Electronic tongue and artificial intelligence have been evaluated as new rapid analysis technologies in complex solution research. The use of nanomaterials to modify the screen-printed carbon electrode (SPCE) increased the specific surface area and electrocatalytic active sites of the electrode. The voltammetric electronic tongue utilizes multiple cross sensitive sensors to describe the overall characteristics of complex samples, and the complementary information between sensors provides rich and comprehensive descriptions for analyzing various components. The use of electronic tongue for rapid analysis of water quality has gradually become a trend, but the overlapping phenomenon of peaks formed by the similar redox potential of each component in water and the interference of the substrate makes it difficult to obtain effective information of each component in water quality. Here, we propose a Fused Convolutional Transformer model (FCvT) that removes the limitation of overlapping peaks by fusing local features and global complementary features to quantify components in complex solutions. The performance of FCvT was evaluated by testing synthetic water samples consisting of four standardized substance solutions and compared with partial least squares (PLS), support vector regression (SVR), artificial neural networks (ANN), and convolutional neural networks (CNN). The results show that the FCvT obtains the lowest mean absolute percentage error in the set of independent tests.The average quantization error of FCvT is reduced by 34.4 % relative to other methods.

Revitalizing Freshwater Ponds: Assessing Physical Structures, Water Quality Parameters and Sustainable Bio- Recharging Strategies Through Community Engagement

Purpose: The purpose of this study is to revitalize the freshwater ponds by assessing physical structures, water quality parameters and sustainable bio- recharging strategies through community engagement. Research Design: The study employs a combination of exploratory and descriptive research approaches. Sampling was conducted among the individuals who are in direct access with the freshwater ponds. Methodology: Data collection involved direct surveys using a structured questionnaire and the water samples were collected from the selected regions Results: The results reveal that there are ponds that need immediate restoration and rejuvenation in order to sustainably take up the matter of a balanced ecosystem for the better survival and coexistence of individual species along with mankind by proper maintenance of the water body for a better tomorrow. The water quality analysis shows significant causes of deterioration due to ecological instability and overuse of the resources by improper management strategies which resulted in loss of biodiversity and habitat loss. Originality of Research: This study records the revitalization of freshwater ponds by assessing physical structures, water quality parameters, and sustainable bio- recharging strategies through community engagement.

Studi Kualitas Fisis Air Sungai Leang Lonrong Kawasan Karst Pangkep Taman Nasional Bantimurung Bulusaraung

This research is an experimental research with a survey type aimed to analyse water quality of the Leang Lonrong River reviewed from smell, taste, Total Dissolved Solid (TDS), turbidity, temperature, color, clarity, and UV index, as well as to describe community activities as factors that influence the water quality of the Leang Lonrong River in its utilization. This study was carried out in the Region Tour Leang Lonrong in the Panaikang Village, Minasatene District, Pangkep Regency, which is one of part Pangkep Karst Area Bantimurung Bulusaraung National Park. In the study, the measurement of water quality for sanitary hygiene needs and public bath reviewed its physical parameters by Regulation Minister Health No. 32 of 2017. Obtained results study that physical parameters water of Leang Lonrong River for sanitary hygiene needs own quality that meets requirement, because the water no smells, no taste, has an average TDS of 6.44 mg/L, an average turbidity of 1.71 NTU, an average temperature of 24.14 °C, and average color 0 TCU. The water of Leang Lonrong River for necessity public bath is no fulfill appropriate water quality standard raw quality. This caused some parameters not to fulfill the standard, namely the parameter of clarity is an average of 0.136 meters deep and UV index parameters more than 3 on at 11:00 a.m and 1:00 p.m in Central Indonesia Time (WITA).

Microbial community dynamics and nitrogen transformation associated with turbid-turned culture stage of Pacific white shrimp, Litopenaeus vannamei in small greenhouse farms

Small greenhouse farms have experienced rapid expansion of Litopenaeus vannamei culture in China in recent years. To investigate microbial community dynamics and nitrogen transformation during the early critical stage, known as the turbid-turned stage in small greenhouse farms, microbial samples from the water column of small greenhouses were analyzed by 16S rRNA and 18S rRNA high-throughput sequencing, along with the detection of ammonia nitrogen in water. The results of water quality analysis revealed that accumulated ammonia nitrogen was converted to nitrite as the water color transitioned from clear green to turbid brown. High-throughput sequencing data demonstrated a shift in dominant microorganisms from zooplankton to algae and bacteria during the turbid-turned stage, and the main functional microbial taxa, such Marivita and Chlorella, were enriched. The results of Linear Discriminant Analysis Effect Size and canonical correlation analyses indicated that the compositions of key microbial communities were correlated with environmental factors. Furthermore, analysis of functional genes indicated that ammonia-oxidizing bacteria were present in the early growth stage, while the abundance of nitrite-oxidizing bacteria was low during the early culture stage. Overall, the findings suggest that microorganisms during the turbid-turned stage in small greenhouses may play a crucial role in regulating water quality and promoting shrimp health within a stable small greenhouse environment.

Smartphone enabled machine learning approach assisted copper (II) quantification and opto-electrochemical explosive recognition by Aldazine-functionalized chemobiosensor

An Aldazine-based optoelectrochemical sensor, BMH (1-(quinolin-4-ylmethylene)hydrazono)methyl)naphthalen-2-ol) has been introduced herein for selective detection of aqueous copper (Cu2+) and 2, 4, 6-Trinitrophenol (TNP) at an ultra-low level detection limit (0.09 ppm for Cu2+ and 0.019 ppm for TNP). Multichannel recognition aptitude of the chemosensor (BMH) towards both Cu2+ and TNP along with bountiful practical applications ascertained it as an innovative one in the environmental and biomedical domains. BMH can detect Cu2+ in water, fetal bovine serum, and human urine samples, while explosive TNP can be identified in water, soil, and matches powder. The intracellular Cu2+ and TNP recognition efficiencies of BMH have been investigated in human lung cancer cell lines (A459). The hassle-free smartphone ensemble machine learning approach for Cu2+quantification has been introduced which would certainly be a significant addition in the domain of water quality analysis. Moreover, the ethylenediaminetetraacetic acid (EDTA) mediated reversibility of the probe could serve as a logic gate imitating electrical circuitry.

Interactive Changes in Climatic and Hydrological Droughts, Water Quality, and Land Use/Cover of Tajan Watershed, Northern Iran

In response to novel and complex uncertainties, the present research is conducted to characterize the most significant indicators of watershed health including drought, water quality, and vegetation for the Tajan watershed, Mazandaran, Iran. The Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are, respectively, used to quantify the meteorological and hydrological droughts in the present (1993–2020) and future (2023–2050) employing optimistic RCP2.6 and pessimistic RCP8.5 scenarios. To concoct discharge data for the future, IHACRES v1.0 software is used with a Nash–Sutcliffe coefficient (NSE) of 0.48 and a coefficient of determination (R2) of 0.58. Maps of land use and Normalized Difference Vegetation Index (NDVI) are also prepared using Landsat images. Subsequently, the surface water quality is assessed using AqQA v1.1.0 software. The results show the difference in the severity of future meteorological droughts in different stations. In addition, the predominance of non-drought (SDI ≥ 0) or mild drought (−1 ≤ SDI < 0) is indicated for future hydrology. The land use changes show a decrease in rangeland (−5.47%) and an increase in residential land (9.17%). The water quality analysis also indicates an increase in carbonate ions in the watershed outlet. Communicating the relationships between study indicators, which is a big gap in the current watershed management approach, avoids future failures and catastrophes.

Polluted River Water Treatment Via Pilot-Scale Membrane Photocatalytic Reactor (MPR) Incorporated Zno-Kaolin Under Different Light Intensity

Polluted river water treatment utilizes a mix of physical, chemical, and biological processes and activities. Conventional systems, including coagulation, flocculation, sedimentation, filtration, and disinfection, have several limitations. Hence, the Membrane Photocatalytic Reactor (MPR) is one of the most promising methods for polluted river water treatment. ZnO-Kaolin nanoparticles served as great photocatalysts for MPR performance. This study focuses on the pilot-scale hybrid MPR treatment of polluted river water under different light intensities (100, 125, and 225 watts) and irradiation times (20, 30, and 40 minutes). The treated water quality analysis is based on the Environmental Quality Act 1974 (EQA 1974). The kinetic rate was also investigated using pseudo-first-order and pseudo-second-order models. It was found that 225 watts and 30 minutes were the optimum values. The pilot scale hybrid MPR proved to fit well with the pseudo-second-order kinetic models, suggesting that the degradation follows a chemisorption mechanism. In conclusion, we believe the hybrid MPR pilot-scale system will enhance efficiency in cleaning dirty river water, all while meeting the standards of the EQA 1974.

ENVIRONMENTAL IMPACTS OF OIL SPILLS ON MARINE ECOSYSTEMS: A CASE STUDY OF THE NIGER-DELTA ENVIRONMENT.

This work documented the effects of oil spills on the aquatic environment by analyzing changes in oil density, water chemistry and species abundance for 15 days in two locations in the Niger Delta. The main rationale was to assess the oil spill's environmental implications and determine the measures that should be implemented. Samples collected daily enabled measurements of the oil content, pH, and dissolved oxygen (DO) on the water surface. Key equipment included spectrophotometers for oil concentration analysis and multiparameter water quality tools for pH and DO measurements. Statistical tools such as regression analysis and ANOVA were employed to evaluate the data. Results indicate a significant decline in oil concentration at both sites. The results of Site A were reduced from 250 µg/L to 170 µg/L, having a regression slope of -5.33 µg/L per day (p < 0.01). Site B's concentration dropped from 320 µg/L to 250 µg/L, with a slope of -4.67 µg/L per day (p < 0.01). Water quality also deteriorated, with Site A’s pH dropping from 7.8 to 6.4 and DO from 8.5 mg/L to 7.1 mg/L. Site B exhibited similar trends, with pH decreasing from 7.9 to 6.5 and DO from 8.6 mg/L to 7.2 mg/L. The results of the ANOVA analysis indicated that the fish population significantly declined at both sites and to this extent, Site A lost eight kinds of fish while Site B lost only 10(p<0.01). Therefore, the study found that the oil spill adversely impacts water quality and the nature of various organisms. These discoveries would help in the current state of knowledge by quantifying the effects of oil spillage and proposing an improved approach to environmental management, which includes but is not limited to effectively mitigating the effects of oil spillage by increasing the monitoring of the effects and rendering timely remediation solutions, and engaging various stakeholders, enforcing strict measures and constantly monitoring the environment.

Integrating of the domestic goat into the rice-fish coculture systems: Rice paddy yield, diet, growth parameters, and intestine microbiota of common carp Cyprinus carpio (Linnaeus, 1758)

Feeding the World while ensuring food security and biodiversity conservation is one of the sustainable development challenges. Integrating farming systems holds great promise for enhancing agricultural sustainability. To reach this goal, the domestic goat (Capra hircus) was integrated into the rice fish system. Four plots each 5 m × 10.65 m were made in two experimental systems, goat-rice-fish (GRF) and rice-fish (RF). We compared the impacts on rice productivity, fish growth performance, water quality, dietary patterns, and gut microbiota composition between both systems. On the one hand, the rice yield indicators, survival rate (SR), specific growth rate (SGR), pH temperature, dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP) were compared. On the other hand, the frequency of occurrence (FOC) of food items of fish (Cyprinus carpio), and the microbiota communities in goat faeces (GoFe) and fish intestines were also examined and compared among both systems. The results showed that the rice yield, the number of filled spikelets per panicle, the number of panicles per land area, and the 1000-grain weight were significantly increased with the integration of goat in the rice-fish coculture system. The SR of fish under the goat-rice-fish system was 12.12% significantly higher than that of the fish under the rice-fish system, as well as the SGR of fish in the GRF system was 21.85% significantly higher than that of the rice-fish system. However, the water quality analyses indicated that the integration of goats significantly increased the concentration of TN and TP. The DO concentration in the GRF system was 25.93% lower than that in the RF system. Dietary analysis of the common carp in goat-rice fish systems revealed shifts in foraging behaviors and diet composition compared to the rice-fish system. Furthermore, the study showed higher diversity in the gut microbiota communities of fish in response to goat-rice-fish integration. In conclusion, this comprehensive investigation underscores the complex and interconnected dynamics within the goat-rice-fish integrated system. It highlights the importance of balanced management practices to harness the system's potential for improved rice yield, fish ecology, and water quality. The findings contribute to a deeper understanding of sustainable integrated farming practices and inform strategies for optimizing the productivity and ecological resilience of this integrated agroecosystem.

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

The Chemical Analysis of Water Quality of India

The Chemical Analysis of Water Quality of India

Water Quality Analysis Based On Diversity and Abundance of Macrozoobenthos In Way Semah River Waters of Pesawaran

Water Quality Analysis Based On Diversity and Abundance of Macrozoobenthos In Way Semah River Waters of Pesawaran

Submerged nanoporous anodized alumina structure for solar-powered desalination.

Development of nanoporous structures utilizing a single step of anodization technique is well recognized as a cost-effective and straightforward approach for several applications. In the current work, anodized alumina was developed with nanoporous structure by utilizing oxalic acid as an electrolyte with a continuous voltage of 40 V. The formed nanoporous structure was subjected to desalination application because of its high absorbance of broadband solar spectrum energy. The desalination setup consists of two solar stills namely conventional and modified. The developed structure is placed in the modified still to examine its performance. It was observed that the structure distributing heat to surrounding water by absorbing photon energy from the sun through the nanopores and giving an efficient pathway to the water vapours for developing effective desalination. The nanoporous structure having ~ 45 nm average diameter. Furthermore, the band gap energy of nanoporous structure was found to be ~ 2.5 eV (absorption spectrum fitting) and ~ 2.8 eV (Tauc plot). The nanoporous structure possess the visible light spectra in solar region which helps the band gaps of nanoporous structure to provide an additional supply of energy for generating more water to evaporate. Moreover, the Urbach energy of the structure is 0.5 eV which reveals less defects in the modified still. The overall distillate yield of modified still was increased to 21% in contrast to conventional. Water quality analysis was also carried out before and after the desalination experiments, and the results were within acceptable limits set by World Health Organization (WHO).

Effective and mechanistic insights into shale gas wastewater reverse osmosis concentrate treatment using ozonation-biological activated carbon process

Reverse osmosis (RO) plays a pivotal role in shale gas wastewater resource utilization. However, managing the reverse osmosis concentrate (ROC) characterized by high salinity and increased concentrations of organic matter is challenging. In this study, we aimed to elucidate the enhancement effects and mechanisms of pre-ozonation on organic matter removal efficacy in ROC using a biological activated carbon (BAC) system. Our findings revealed that during the stable operation phase, the ozonation (O3 and O3/granular activated carbon)-BAC system removes 43.6–72.2 % of dissolved organic carbon, achieving a 4–7 fold increase in efficiency compared with that in the BAC system alone. Through dynamic analysis of influent and effluent water quality, biofilm performance, and microbial community structure, succession, and function prediction, we elucidated the following primary enhancement mechanisms: 1) pre-ozonation significantly enhances the biodegradability of ROC by 4.5–6 times and diminishes the organic load on the BAC system; 2) pre-ozonation facilitates the selective enrichment of microbes capable of degrading organic compounds in the BAC system, thereby enhancing the biodegradation capacity and stability of the microbial community; and 3) pre-ozonation accelerates the regeneration rate of the granular activated carbon adsorption sites. Collectively, our findings provide valuable insights into treating ROC through pre-oxidation combined with biotreatment.

A Data-Driven Method for Water Quality Analysis and Prediction for Localized Irrigation

A Data-Driven Method for Water Quality Analysis and Prediction for Localized Irrigation

Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies

AbstractIdentifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating water quality index (WQI). In this context, machine learning–based models are introduced to predict the WQ factors of Southeastern Black Sea Basin. The data comprising monthly samples of different WQ factors were collected for 12 months at eight locations of the Türkiye region in Southeastern Black Sea. The traditional evaluation with WQI of surface water was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) and pi-sigma artificial neural networks (PS-ANNs) were used to predict WQI, and the accuracy of the proposed algorithms were compared. SMN model and PS-ANNs were used for WQ prediction modeling for the first time in the literature. According to the results obtained from the proposed ANN models, it was found to provide a highly reliable modeling approach that allows capturing the nonlinear structure of complex time series and thus to generate more accurate predictions. The results of the analyses demonstrate the applicability of the proposed pi-sigma model instead of using other computational methods to predict WQ both in particular and other surface water resources in general.