The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

Cosmetic industries are highly committed to finding natural sources of functional active constituents preferable to safer materials to meet consumers’ demands. Marine macroalgae have diversified bioactive constituents and possess potential benefits in beauty care products. Hence, the present study was carried out to characterize the biochemical profile of marine macroalga Chaetomorpha crassa by using different techniques for revealing its cosmetic potentials. In results, the FTIR study characterized the presence of different bioactive functional groups that are responsible for many skin-beneficial compounds whereas six and fifteen different important phycocompounds were found in GCMS analysis of ethanolic and methanolic extracts, respectively. In the saccharide profile of C. crassa, a total of eight different carbohydrate derivatives were determined by the HRLCMS Q-TOF technique, which showed wide varieties of cosmetic interest. In ICP AES analysis, Si was found to be highest whereas Cu was found to be lowest among other elements. A total of twenty-one amino acids were measured by the HRLCMS-QTOF technique, which revealed the highest amount of the amino acid, Aspartic acid (1207.45 nmol/mL) and tyrosine (106.77 nmol/mL) was found to be the lowest in amount among other amino acids. Their cosmetic potentials have been studied based on previous research studies. The incorporation of seaweed-based bioactive components in cosmetics has been extensively growing due to its skin health-promoting effects.

The shortage of natural aggregates has compelled the developers to devote their efforts to finding alternative aggregates. On the other hand, demolition waste from old constructions creates huge land acquisition problems and environmental pollution. Both these problems can be solved by recycling waste materials. The current study aims to use recycled brick aggregates (RBA) to develop eco-friendly pervious concrete (PC) and investigate the new concrete’s structural performance and pore structure distributions. Through laboratory testing and image processing techniques, the effects of replacement ratio (0%, 20%, 40%, 60%, 80%, and 100%) and particle size (4.75 mm, 9.5 mm, and 12.5 mm) on both structural performance and pore feature were analyzed. The obtained results showed that the smallest aggregate size (size = 4.75 mm) provides the best strength compared to the large sizes. The image analysis method has shown the average pore sizes of PC mixes made with smaller aggregates (size = 4.75 mm) as 1.8–2 mm, whereas the mixes prepared with an aggregate size of 9.5 mm and 12.5 mm can provide pore sizes of 2.9–3.1 mm and 3.7–4.2 mm, respectively. In summary, the results confirmed that 40–60% of the natural aggregates could be replaced with RBA without influencing both strength and pore features.

Soil is a heterogeneous material, and its properties are vital from an agricultural perspective and for groundwater management. However, limited studies have been performed on the soil characteristics (soil texture, water-holding capacity, and soil compositions) of a single pumping well, especially in Malaysia. This article focuses on the soil characteristics and elemental analysis of a single borehole with 11 samples collected around Labu Kubong, Perak. The soil properties were analyzed in the context of particle size distribution (PSD) using the laser diffraction method (LDM), as well as soil composition for elemental analysis using a scanning electron microscope coupled with an energy dispersive X-ray (SEM-EDX). The LDM results revealed the average percentage of clay, silt, and sand to be 0%, 6%, and 94%, respectively, indicating most particles comprised sand particles which in percentages demonstrated a sandy texture with less silt content. Additionally, the water holding capacity is low because of major coarse sand particles in alluvial formations. Moreover, SEM-EDX outcomes displayed an average percentage of elemental composition reported as follows: C (40.77%), O (34.33%), Si (10.66%), Al (5.82%), Fe (1.10%), K (1.10%), As (0.05%), Na (0.04%), and Be (5.62%). Consequently, SEM-EDX outcomes showed these elements were derived from silicified quartz, feldspar, and iron-bearing minerals that originated from shale formations, and the presence of carbon indicates peat formation. Therefore, this study provides information on a single pumping well from an irrigation practice, and this study also recommends regional to global scale studies for supporting sustainable groundwater development worldwide.

In the present study, the quality of the surface water of the Gomti river (Lucknow, India) was investigated. Lucknow is situated in the centre of Uttar Pradesh, which is most the populated state in India. The locality has experienced rapid, unregulated development activities and population growth in recent decades, both of which have had a negative impact on its ecosystem and environment. Continuous monitoring is required to maintain the ecosystem at the desired level. Nine samples of river water were collected from the Gomti River in Lucknow, and they were analysed for a total of nine different characteristics, including pH, turbidity (Tur), dissolved oxygen (DO), total dissolved solids (TDSs), chemical oxygen demand (COD), chloride ion (Cl-) concentration, temperature (T), biochemical oxygen demand (BOD₅) and total hardness (TH). The observed data were analysed using multivariate statistical methods. A cluster analysis (CA) was used to sort the sampling locations into different groups, and a principal component analysis (PCA) was used to find the different sources of pollution. Using a cluster analysis, all the water quality parameters were divided into three groups. Cluster 1 represented the less polluted sites, cluster 2 represented the moderately polluted sites and cluster 3 represented the highly polluted sites. Sampling sites SS8, SS4, S99 and SS7 were highly polluted because of nearby pollution sources such as domestic wastewater and runoff storm water. The principal component analysis yielded two meaningful components that explained 82.4% of the total variation in the data. The first factor and second factor explained 59.022 and 23.363 percentages of the total variance, respectively. It was noticed that major sources of pollution for the Gomti river are storm water runoff and the release of domestic and industrial wastewater from residents and industries, respectively. This study will help policy makers to ensure sustainable practices and reduce negative impacts on the availability and quality of water, allowing for the most efficient use of the Gomti River.

With increasing population and industrialization, the water quality of freshwater sources like rivers, lakes, and ponds is becoming increasingly degraded. Most of the rivers in India are becoming polluted, including the Godavari. With the construction of dams, new industries and unsustainable agricultural practices in the Godavari basin, the water characteristics are degrading spatially and temporally. The present study emphasizes the analysis of water quality parameters like temperature, pH, Dissolved Oxygen (DO), conductivity, Biological Oxygen Demand (BOD), nitrate, and faecal coliform concentration in the Godavari basin. This was achieved by analysis of data taken from the Central Pollution Control Board, India (CPCB) for 21 stations around the Godavari basin over a span of five years from 2015 to 2019. The Pearson Correlation coefficient for the water quality parameters was assessed to study the relationship among the parameters. Variation in the water quality parameter is observed from the graphs for each station for respective years. It was found that conductivity and DO, temperature and pH and DO and faecal coliform are negatively correlated. It was also observed that DO has a negative correlation with pH, BOD and faecal coliform, indicating the utilization of dissolved oxygen at higher rates due to increasing degradation of organic matter by aerobic microorganisms in the river. One-way ANOVA was applied to find out significant temporal variations and it was observed that temperature, pH, and faecal coliform level had significantly changed the overdue course of time (F(4, 115) = 2.451, p < 0.05). The obtained results from the analysis indicate that the selected water quality parameters have varied significantly spatially, whereas temporally, according to the ANOVA coefficient, only temperature, pH and faecal coliform had shown significant differences during the selected timeframe. Hence, the present study highlighted the deteriorating water quality of the Godavari River over time.