ABS was the first surfactant used in detergent formulations, but because its molecular structure is branched, it is difficult to decompose biologically, making ABS a toxic compound for the environment. This study aims to remove MBAS surfactant, using a combination of phytoremediation and filtration methods to remove surfactant (MBAS) Chemical Oxygen Demand (COD) from detergent wastewater by optimizing operating factors such as pH, contact time, plant type, and filter media. Water lettuce (Pistia stratiotes) and water hyacinth (Eichhornia crassipes) were selected as plant species and silica-activated carbon was used as filter media. Water lettuce and hyacinth were grown in a 10-liter reactor with detergent wastewater samples for 6 and 12 days. Filter media are placed in the reactor in use, and aeration is done. The efficiency for reducing COD was 81.73%, and the efficiency for surfactant was 99.42% for each experiment, which was thought to be because of plant adsorption and filtering processes. The water lettuce (Pistia stratiotes) plant had the maximum adsorption capability for all the qualities evaluated, with a surfactant content in the roots of 27543.24 (mg/kg MBAS), compared to the water hyacinth plant, which only absorbed 2597.95 (mg/kg MBAS).

Short-lived climate pollutants (SLCPs) have significant effects on climate, human health, and the environment. In Morocco, steps are being taken to reduce SLCP emissions, but effective policymaking requires a thorough understanding of emission sources and trends. In this paper, we present a study on the production of a database on SLCP emissions in Morocco, as well as the development of scenarios to project these emissions using LEAP software. The results of this analysis allow us to better understand the emissions sources and evaluate the impact of different emission reduction policies.

Algal biofuels are a promising renewable feedstock to produce energy that can supplement future energy demands greatly. The present study aims to utilize Dunaliella salina, a hypersaline, unicellular greenish-orange micro-algae, to produce bio-oil. F/2 nutrient media and trace metal and vitamin solution under carbon-dioxide-rich conditions were used to cultivate the microalgae. Ultrasonic extraction method at 60 Hz for 90 min isolated 650 mL of bio-oil. A single-stage based-catalyzed transesterification process with methanol and sodium hydroxide yielded 380 mL of Pure Dunaliella salina biodiesel at % an extraction efficiency of 87%. The Phytochemical screening on the cultivated Dunaliella sp. was performed to understand its feasibility to be used as a fuel for IC engines. Furthermore, the obtained biodiesel was characterized using Fourier Transform Infrared Spectrometer (FTIR), Gas Chromatography Mass Spectrometer (GCMS), and Nuclear Magnetic Resonance (NMR) spectral analysis.

This study aims to determine how vulnerable groundwater in Khartoum is to contamination. For this purpose, the DRASTIC Index idea was used. A descriptive cross-sectional analytical analysis is designed in this study. A total of 279 boreholes were sampled from a total of 1015 boreholes (27.5 percent). The following criteria were utilized to define the DRASTIC Index: depth, net recharge, aquifer media, soil texture, terrain, video media, and soil conductivity. Standard bacteriological test methodologies were used for groundwater. The biological data from the 279 boreholes revealed that total coliform, thermo-tolerant coliform, and E. coli were found in 34.4 percent, 18.6 percent, and 0.36 percent of the boreholes, respectively. Bacteriological contamination is common in Sharge Elnile, although only a few cases have been reported in Khartoum. According to the study, the bulk of boreholes in Khartoum State were built without any criteria. Many sources of contamination were discovered within a radius of less than 120 meters, which was deemed to violate Khartoum State’s Environmental Health Law of 2002. For this reason, bacteriological contamination is common in Sharge Elnile, although only a few cases have been reported in Khartoum.

In this paper, greywater samples are collected from the kitchens of different types of buildings (residential and commercial) located in different districts within the city of Jeddah, Saudi Arabia. The collected samples are analyzed and compared with the potable water from the same region. The parameters investigated are pH, conductivity, total solids (TS), total dissolved solids (TDS), total suspended solids (TSS), total hardness, temporary hardness, permanent hardness, alkalinity, chloride, and biochemical oxygen demand (BOD). It was found that the amount of total suspended solids is very high in the greywater samples. It shows the presence of both temporary and permanent hardness. Their alkalinity values are greater than hardness. It may be due to the number, lifestyle, age of the occupants, presence of children, and social and cultural behavior of residents. The concentration of BOD level is very low, which shows that the greywater samples have lower concentrations of organic compounds. Design details of the greywater treatment plant are suggested based on the results of the analysis. This includes a screening chamber, grit chamber, settling tank, and filtration unit. The treated greywater is recommended for reuse for gardening, landscaping, and toilet flushing purposes.

To become the fastest-growing large economy in the world, India has set a target growth rate of 9%, reaching an economy of $5 trillion by 2024-25. It is an immense challenge to meet the growth target and keep the CO2 emissions under control. The present paper aims to discover the determinants for explaining CO2 emissions in India by conducting a complete decomposition analysis, where the residuals are fully distributed to the determinants for the country from 1990-2018. The analysis reveals that the biggest contributor to the rise in CO2 emissions in India is the expansion of the economy (scale effect). The intensity of CO2 and the change in the composition of the economy, which nearly move in tandem, also contribute to the rise in CO2 emissions, although more slowly. A declining energy intensity of the Indian economy is responsible for a considerable reduction in CO2 emissions. As a typical result for an upcoming economy, this paper did not find evidence for an environmental Kuznets curve. This implies that continued economic growth will lead to increased CO2 emissions.

This first-of-its-kind study systematically assesses the abundance and characteristics of Microplastics (MPs) in different categories of informal open drains (nallas) carrying different liquid waste streams from different functional areas of an Indian city. Such drains are part of the informal urban drainage system that carries wastewater, stormwater, industrial effluent, and rural runoff. Logistical and locational limitations of traditional wastewater (WW) sampling methods severely limit their application in open drains. To overcome sampling challenges owing to complex geography, vast drainage network spread across different functional areas of the entire city, and local challenges, appropriately modified sampling strategies were adopted to collect samples from 35 open WW drains (small/local, intermediatory, and large). MPs (50μm-5mm) were present in a bucket, and net samples obtained from all 35 WW drains. The average MP concentration in WW drains was 4.20 ± 1.40 particles/L (bucket samples) and 5.19 ± 1.32 particles/L (net samples). A declining trend of MPs abundance was observed from larger to smaller drains, confirming that smaller and intermediatory drains (carrying WW from different functional areas of the city) are discharging their MP loads into larger drains. Intermixing different WW streams (municipal WW, stormwater surface runoff, agricultural runoff, and industrial WW) increases MP levels in drains. The local riverine ecosystem is being put at risk by a daily MPs load of 12.6 x 108 particles discharged from 9 larger drains into the local river Kharun. To protect the riverine ecosystem, controlling the high daily MPs load from such drains is important. Diversion of WW drains through constructed wetlands built near river banks can be a cost-effective solution. Because the entire Indian subcontinent and parts of Africa rely mainly on such drains having similar characteristics and local conditions, the findings of this study reflect the status and pattern of MPs pollution in informal drains of the entire Indian subcontinent and can be used by stakeholders and governments to take mitigative and preventive measures to manage the MPs pollution and protect the local riverine ecosystem.

Over the past decades, energy forecasting has attracted many researchers. The electrification of the modern world influences the necessity of electricity load, wind energy, and solar energy forecasting in power sectors. Energy demand increases with the increase in population. The energy has inherent characteristics like volatility and uncertainty. So, the design of accurate energy forecasting is a critical task. The electricity load, wind, and solar energy are important for maintaining the energy supply-demand equilibrium non-conventionally. Energy demand can be handled effectively using accurate load, wind, and solar energy forecasting. It helps to maintain a sustainable environment by meeting the energy requirements accurately. The limitation in the availability of sufficient data becomes a hindrance to achieving accurate energy forecasting. The transfer learning strategy supports overcoming the hindrance by transferring the knowledge from the models of similar domains where sufficient data is available for training. The present study focuses on the importance of energy forecasting, discusses the basics of transfer learning, and describes the significance of transfer learning in load forecasting, wind energy forecasting, and solar energy forecasting. It also explores the reviews of work done by various researchers in electricity load forecasting, wind energy forecasting, and solar energy forecasting. It explores how the researchers utilized the transfer learning concepts and overcame the limitations of designing accurate electricity load, wind energy, and solar energy forecasting models.

The quality of surface water has a significant impact on human health and the entire ecological system. Sewer spillages from the surrounding informal settlements discharging into the river, carrying high concentrations of fecal coliforms, are one of the major causes of extreme pollution in the rivers of South Africa. These informal settlements are common in many developing countries, and they are usually located near waterways to compensate for basic demands for water, sanitation, and recreational space, where municipal infrastructure lags behind urban growth. One major problem has been poor sanitation and poor waste disposal practices in the informal settlements, which has led to the contamination of water resources. This study aims to assess the extent to which poor sanitation in informal settlements impacts the water quality of South African rivers, given the rapid rise in population and unemployment rate. The study also highlights health and environmental issues in the local regions caused by poor sanitation. Contamination of water bodies is associated with serious health problems and fatalities. Therefore, there is a need for frequent monitoring and management of waste products discharged into the neighboring aquatic environments.

In this study, waste tires were used to develop activated carbon for the adsorption of Cr(VI) from aqueous solutions, and an artificial neural network (ANN) model was applied to predict the adsorption efficiency of waste-tire activated carbon (WTAC). SEM and FTIR were used to characterize the developed WTAC. A three-layer ANN with different training algorithms and hidden layers with different numbers of neurons was developed using 79 data sets gathered from batch adsorption experiments with different initial Cr(VI) ion concentrations, contact periods, temperatures, and doses. Conjugate gradient backpropagation of Powell-Beale restarts (traincgb) was found to be the best training algorithm among all the training algorithms, with an RMSE of 5.894 and an R2 of 0.985. The ANN topology had 4, 8, and 4 neurons in the input, hidden, and output layers. The correlation coefficient of the ANN models of Cr(VI) ion adsorption efficiency is 0.977.