Explore the vast range of titles available.

This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

The rapid growth of population and industrialization have intensified the problem of water pollution globally. To meet the challenge of industrialization, the use of synthetic dyes in the textile industry, dyeing and printing industry, tannery and paint industry, paper and pulp industry, cosmetic and food industry, dye manufacturing industry, and pharmaceutical industry has increased exponentially. Among these industries, the textile industry is prominent for the water pollution due to the hefty consumption of water and discharge of coloring materials in the effluent. The discharge of this effluent into the aquatic reservoir affects its biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), total suspended solids (TSS), and pH. The release of the effluents without any remedial treatment will generate a gigantic peril to the aquatic ecosystem and human health. The ecological-friendly treatment of the dye-containing wastewater to minimize the detrimental effect on human health and the environment is the need of the hour. The purpose of this review is to evaluate the catastrophic effects of textile dyes on human health and the environment. This review provides a comprehensive insight into the dyes and chemicals used in the textile industry, focusing on the typical treatment processes for their removal from industrial wastewaters, including chemical, biological, physical, and hybrid techniques.

The water quality index is one of the prominent general indicators to assess and classify surface water quality, which plays a critical role in river water resources practices. This research constructs a hybrid artificial intelligence model namely sequential minimal optimization-support vector machine (SMO-SVM) along with random forest (RF) as a benchmark model for predicting water quality values at the Wadi Saf-Saf river basin in Algeria. The fifteen input water quality datasets such as biochemical oxygen demand (BOD), oxygen saturation (OS), the potential for hydrogen (pH), chemical oxygen demand (COD), chloride (Cl − ), dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), nitrate-nitrogen (NO 3 -N), nitrite-nitrogen (NO 2 -N), phosphate (PO 4 3− ), ammonium (NH 4 + ), temperature (T), turbidity (NTU), and suspended solids (SS) were employed for constructing the predictive models. Different input data combinations are evaluated in terms of predictive performance, using a set of statistical metrics and graphical representation. Results show that less than 40% of samples were observed to be poor quality water during the dry season in downstream northeastern part of the basin. The findings also show that the RF model mostly generates more precise water quality index predictions than the SMO-SVM model for both training and testing stages. Although thirteen input parameters attain the optimal predictive performance ( R 2 testing = 0.82, RMSE testing = 5.17), a couple of five input parameters, e.g., only pH, EC, TDS, T, and saturation, gives the second optimal predictive precision ( R 2 test = 0.81, RMSE testing = 5.55). The sensitivity analysis results indicate a greater sensitivity by the all input variables chosen except NO 2 − of the predictive outcomes to the earlier influencing water quality parameters. Overall, the RF model reveals an improvement on earlier tools for predicting water quality index, according to predictive performance and reducing in the number of input variables.

Conductivity (EC) and total dissolved solids (TDS) are water quality parameters, which are used to describe salinity level. These two parameters are correlated and usually expressed by a simple equation: TDS = k EC (in 25 °C). The process of obtaining TDS from water sample is more complex than that of EC. Meanwhile, TDS analysis is very important because it can illustrate groundwater quality, particularly in understanding the effect of seawater intrusion better than EC analysis. These conditions make research in revealing TDS/EC ratios interesting to do. By finding the ratio value, TDS concentration can be measured easily from EC value. However, the ratio cannot be defined easily. Previous research results have found that the correlation between TDS and EC are not always linear. The ratio is not only strongly influenced by salinity contents, but also by materials contents. Furthermore, the analysis of TDS concentration from EC value can be used to give an overview of water quality. For more precision, TDS concentrations need to be analyzed using the gravimetric method in the laboratory.

Industrial effluents are one of the foremost concerns relating to the anthropogenic environmental pollution. The effluents from the tanning and textile industries in Dhaka, Bangladesh, were characterized chemically and physicochemically with multivariate statistical techniques. The concentrations of heavy metals viz. , Pb, Cd, Cr, Mn, Fe, Ni, Cu, and Zn were determined by atomic absorption spectrometer while concentrations of anions viz. , F − , Cl − , NO 2 − , NO 3 − , and SO 4 2− were measured by ion chromatograph. The physicochemical parameters viz. , temperature, pH, electrical conductivity (EC), salinity, turbidity, dissolved oxygen (DO), and biological oxygen demand (BOD) were measured by a multiparameter meter while total suspended solids (TSS) and total dissolved solids (TDS) were measured gravimetrically. This study showed that effluents from both industries demonstrated high levels of TSS, TDS, EC, and heavy metals. Tannery effluents have lower pH and DO, and higher BOD, Cl − , SO 4 2− , and Cr concentrations while textile dyeing effluents have higher pH, NO 2 − , and NO 3 − concentrations, compared to the standard limits promulgated by the Bangladesh government. Multivariate statistical techniques such as cluster analysis and principal component analysis along with the correlation matrices showed significant association among the measured parameters and identified pollution sources as well as effluent types in the study area which could be linked to the processes used in textile dying and tanning industries. This study will be useful for identifying pollutants emanating from the two industries and will guide future industrial aquatic studies where multiple industrial runoffs are concerned.

Human health is the main concern related to use of crop products irrigated with contaminated irrigation sources. Present research has been conducted to explore heavy metal status of sewage and industrial wastewater being used up for irrigation purpose in the peri-urban areas of the district Dera Ghazi Khan which has not been explored widely before. The analysis also followed heavy metal detection in the subsequent irrigated soil and vegetables/crop plants in relation to assessment of health risk to the consumer to plan the future monitoring in this area. An unremitting boost of heavy metals into the environment from wastewater irrigation has become a global issue. These heavy metals enter the food chain and pose health assumptions to consumers upon utilization. In the present study, an investigation has been conducted to determine metal concentrations in the wastewater, soil, and different plant species. For wastewater samples, pH, total dissolved solids (TDS), electrical conductivity (EC), and selected heavy metals such as Al, As, Cr, Cu, Fe, Mn, Pb, Zn, and Ni were determined. The mean values of heavy metals in the soil samples were within the WHO/FAO safe limit, while Cr and Pb were the most frequent (100%) among the metals. However, differentiating the sites, the concentration of Cr and Cu, Ni, and Fe were elevated. The metal transfer was highly effective from soil to the growing plants i.e. brinjal, red corn, wheat, tomato, and spinach than other plant species. Among the metals, Cr, Ni, Mn, and Pb in plant samples were exceeding the WHO/FAO safe limit. Health risk index (HRI) have revealed the possible potential risk of heavy metal contaminated plant species in the order of spinach (6.4) > wheat (6.4) > brinjal (5.9) > tomato (4.7) > red corn (4.5) > apple gourd (4.3) > white corn (3.8) > cabbage (3.1) > luffa (2.9). Likewise, HRI of different metals was calculated as Cu (19.6) > Zn (17.9) > Cr (2.95) > Ni (0.85) > Mn (0.48) > Fe (0.15) > Cd (0.11) > Pb (0.05) > As (0.00001). The level of HRI through the use of dietary plants revealed an elevated risk level than the acceptable limit (HRI > 1) for Cu > Zn > Cr in adults. Our findings suggest that there would be a serious health risk to the consumers due to the consumption of these plant species being irrigated with the wastewater. Therefore, a strict regulatory mechanism is proposed for the safety of food plants in the study area including monitoring and recycling of crop plants, and building water treatment plants to remove pollutants and clean wastewater.

Here we present digitization and analysis of the thermal springs of the world dataset compiled by Gerald Ashley Waring in 1965 into a collection of analog maps. We obtain the geographic coordinates of ~6,000 geothermal spring areas, including complementary data (e.g., temperature, total dissolved solids, flow rate), making them available in electronic format. Using temperature and flow rate, we derive the heat discharged from 1483 thermal spring areas (between ~10 −5 and ~10 3  MW, with a median value of ~0.5 MW and ~8300 MW in total). We integrate this data set with other global data sets to study the relationship between thermalism and endogenous and exogenous factors with a supervised machine learning algorithm. This analysis confirms a dominant role of the terrestrial heat flow, topography, volcanism and extensional tectonics. This data set offers new insights and will boost future studies in geothermal energy exploration. Data from 6000 geothermal areas worldwide are analyzed with a machine learning approach. The analysis suggests and confirms a dominant role of the terrestrial heat flow, topography, volcanism and extensional tectonics.

Microalgae have the potential as a source of biofuels due to their high biomass productivity and ability to grow in a wide range of conditions, including wastewater. This study investigated cultivating two microalgae species, Oocystis pusilla and Chlorococcus infusionum , in wastewater for biodiesel production. Compared to Kühl medium, KC medium resulted in a significant fold increase in cellular dry weight production for both O. pusilla and C. infusionum , with an increase of 1.66 and 1.39, respectively. A concentration of 100% wastewater resulted in the highest growth for O. pusilla , with an increase in biomass and lipid content compared to the KC medium. C. infusionum could not survive in these conditions. For further increase in biomass and lipid yield of O. pusilla , different total dissolved solids (TDS) levels were used. Maximum biomass and lipid productivities were achieved at 3000 ppm TDS, resulting in a 28% increase in biomass (2.50 g/L) and a 158% increase in lipid yield (536.88 mg/g) compared to KC medium. The fatty acid profile of O. pusilla cultivated on aerated wastewater at 3000 ppm TDS showed a high proportion of desirable saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA) for biodiesel production. Cultivating microalgae in wastewater for biodiesel production can be cost-effective, especially for microalgae adapted to harsh conditions. It could be concluded that O. pusilla is a promising candidate for biodiesel production using wastewater as a growth medium, as it has high biomass productivity and lipid yield, and its fatty acid profile meets the standard values of American and European biodiesel standards. This approach offers a sustainable and environmentally friendly solution for producing biofuels while reducing the environmental impact of wastewater disposal.