Environmental damage due to the discharge of organic pollutants and heavy metal toxins has become a major topic of concern for the past couple of years. Using just a natural adsorbent to solve wastewater concerns has lately gained popularity as an ecologically acceptable solution that encourages long-term growth. A range of approaches, including adsorption to the surface of agricultural leftovers, have been used to minimize heavy metals in an aqueous medium. Lead is amongst the most hazardous and widely discovered toxic substances in industrial waste. Citrus limetta peel powder, Banana peel powder, and Betel leaf powder were chosen as adsorbents in this study to absorb synthetic lead from an aqueous solution since they are low-cost materials. Our research aims to find natural bio-sorbents that can remove highly hazardous Pb2+ ions from aqueous solutions. The importance of contact time, concentrations, adsorbent-based dose, and pH in the adsorption process is investigated. The adsorption rate for betel leaves, Citrus limetta peel, and banana peel was 5, 10, 15, 20, and 25 g.L-1. Citrus limetta peel (10 g.L-1), banana peel (5 g.L-1), and betel leaf (5 g.L-1) provide the highest lead adsorption. Material characterization is used to determine the lead nitrate process in lead adsorption. The capacity of the lead-adsorbing substances to achieve adsorption equilibrium was assessed and estimated using linear Freundlich and Langmuir isotherms, with the experimental data fitting the Freundlich isotherm models.

Evapotranspiration (ET) plays a significant role in climatic studies, directly influencing the hydrological cycle, energy balance equation, and surface vegetation. ET comprises three components: bare soil or ground evaporation, evaporation, and transpiration, in which vegetation removes water influenced by food grain production. In turn, soil moisture availability depends on precipitation characteristics over land, surface net radiation, and wind speed are the major climatic factors that together determine the magnitude of ET. This controls moisture availability in the lower troposphere, hence atmospheric stability, chances of cloud formation, and precipitation. Though the study of evapotranspiration is important for determining agricultural water consumption and analyzing drought situations, there is a lot of uncertainty in its accurate estimation. Land use/Land cover changes (LULCC) occurring throughout the Indian subcontinent have been found to affect the characteristics of low to moderate rainfall events and surface temperature extremes (Halder et al. 2016). A global warming scenario will change the hydrological cycle, and the impact of anthropogenic factors has also necessitated the need to understand the mechanisms that control changes in ET over India. In this study, we want to analyze the relationship between transpiration and the Normalized Difference Vegetation Index (NDVI) and investigate the relationship between canopy interception with respect to NDVI all over the Indian region. Attempts have been made to assess the impact of changes in climate and LULC on ET and its three components over the Indian region from 1981 till the present time. The monsoon season increases precipitation, and soil evaporation is found to increase at first, along with an increase in NDVI followed by canopy evaporation and transpiration. It is noted that changes in precipitation and LULCC across the Indian subcontinent have contributed significantly to changes in ET in different seasons. As variability in surface net radiation also plays an important role in controlling changes in total ET, it is being investigated.

Oceans and large water bodies have the potential to generate a large amount of green and renewable energy by harvesting the ocean surface properties like wind waves and tidal waves using Wave Energy Converter (WEC) devices. Although the oceans have this potential, very little ocean energy is harvested because of improper planning and implementation challenges. Besides this, monitoring ocean waves is of immense importance as several ocean-related calamities could be prevented. Also, the ocean serves as the maritime transportation route. Therefore, a need exists for remote and continuous monitoring of ocean waves and preparing strategies for different situations. Remote sensing technology could be utilized for a large scale low-cost opportunity for monitoring entire ocean bodies and extracting several important ocean surface features like wave height, wave time period, and drift velocities that can be used to estimate the ideal locations for power generation and find locations for turbulent waters so that maritime transportation hazards could be prevented. To process this large volume of data, Big Data Analytics techniques have been used to distribute the workload to worker nodes, facilitating a fast calculation of the reanalyzed remote sensing data. The experiment was conducted on Indian Coastline. The findings from the experiment show that a total of 1.86 GWh energy can be harvested from the ocean waves of the Indian Coastline, and locations of turbulent waters can be predicted in real-time to optimize maritime transportation routes.

The goal of this study was to examine the water quality for drinking and domestic purposes in the Korba coalfield region of Chhattisgarh, India. The Korba Coalfield region has seen the collection of fifteen groundwater samples from different places. The content of eight metals was determined using ICP-MS instruments: aluminum (Al), barium (Ba), cadmium (Cd), iron (Fe), magnesium (Mn), lead (Pb), nickel (Ni), and zinc (Zn). Spatial distribution maps were produced using GIS software to make it simple to understand the groundwater’s quality. The groundwater samples were collected during the pre-monsoon season and the amount of Al, Ba, Cd, Fe, Mn, Pb, Ni, and Zn exceeded the ideal drinking water standards in a few sites. The elevated metal concentrations in the study region’s groundwater could be hazardous to the quality of water. The HPI value based on mean concentration was calculated to be 21.64, which is significantly lower than the reference pollutant index score of 100. The HPI calculation revealed that 73.33% of groundwater samples had low HPI values, 6.67% had medium HPI values, and the remaining 20% had high HPI values. The correlation between heavy metals and HPI was calculated; HPI is positively correlated with Fe (r > 0.9471), Pb (r > 0.9666), and Zn (r > 0.9634), indicating that these elements contribute significantly more to heavy metal concentration in the various samples examined than the other selected elements. The box plot seems to be a graphical representation of the outcomes of the different parameter concentrations which show the mean, maximum, and minimum metal values. The cluster analysis was performed and it was classified into two clusters. Cluster-1 comprises 14 members (93.33%) of the water samples examined and is distinguished by relatively low Ba (<700 μg.L-1), pH, TDS, Al, Fe, Cd, Mn, Pb, and Zn concentrations. Cluster-II is made up of 1 member (6.67%), which is primarily made up of groundwater samples (GW-10) taken in the KCF region, India. High values of HPI are found in the eastern portion of Chhattisgarh’s KCF region, reflecting the spatial distribution of metals. Heavy metal leaching from open-pit mining and transit routes was observed to have contaminated groundwater in the eastern section of the research region.

The study assessed the status of water quality parameters for an urban water body (Hatirjheel Lake) in Dhaka, the Capital city of Bangladesh. Nine different water samples were collected from nine points of the lake during the dry season in January 2021. Water quality parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), total suspended solids (TSS), total alkalinity, total acidity, total hardness, Ca2+ hardness, free CO2, and dissolved oxygen (DO) were determined for the samples. The status of the parameters is pH (6.51-7.05), EC (510-600 μS.cm-1), TDS (450-590 ppm), TSS (0.0-0.034 mg.L-1), total alkalinity (80-392 mg.L-1), total acidity (224-500 mg.L-1), total hardness (348-452 mg.L-1), Ca2+ hardness (74-162 mg.L-1), free CO2 (730-1170 mg.L-1), DO (2.7-5.5 mg.L-1). However, the DO value at some points of the lake is too less (2.7 mg.L-1 and 3.7 mg.L-1) than the standard value (> 5-6 mg.L-1) of ECR, DoE, which might not be healthy for any water body and aquatic ecosystem. Other water quality parameters are within the permissible limit of WHO and ECR, DoE.

In the fight against global warming, various options for reducing CO2 emissions are being implemented on campus. Furthermore, the management of campus sustainability at the Universitas Negeri Semarang (UNNES), Central Java, Indonesia, should be supported by accurate forecasts of electrical energy consumption. Therefore, this research aims to develop a predictive model to forecast the consumption of electrical energy in reducing CO2 emissions and to determine the factors triggering the increase. The prediction model is developed using Back Propagation Neural Network Artificial (BP-ANN) architecture. Furthermore, the data on the occupancy of lecturers and education staff as well as on students was obtained from the University's staffing and student affairs bureau. Climatic data such as temperature, humidity, wind speed, the duration of irradiation, and the average intensity of solar radiation were obtained per month from the Meteorology, Climatology, and Geophysics Agency of Semarang, Central Java for the 2013-2019 period as input data. The results of the empirical analysis showed an increase in electrical energy consumption from 2020 to 2025. In March, the consumption decreased but increased from April to June and decreased in July. It then increased until November and December, and it decreased every year. The results of CO2 emissions calculated by considering the emission factors from Indonesia's RUPTL-PLN in 2020-2025 showed an increase in electrical energy consumption and the ecological consequences affecting the campus area. Furthermore, the main factors causing the high consumption of electrical energy are the occupancy rate, lecturers, students, and campus employees, as well as local climate influences such as temperature, humidity, wind speed, duration of solar radiation, and intensity of solar radiation. Therefore, developing guidelines to reduce power consumption on campus should be a priority

Vertical flow-constructed wetlands (VFCW) are well-established, cost-effective, and sustainable options for wastewater treatment. Along with organic matter removal, wetlands are helpful in the removal of microbial pathogens. This study focuses on understanding the bacterial pathogen removal efficacy of three different design types of VFCWs and understands the best designs for the efficient removal of pathogens in a tropical climate. The three wetlands studied for removal efficiency were (a) two-stage vertical flow constructed wetland (TSVFCW), (b) Single-stage vertical flow constructed wetland (SSVFCW), and (c) single-stage saturated vertical flow constructed wetland (SSSVFCW). Results revealed that all three types of wetlands were effective in removing pathogenic bacteria. Still, TSVFCW was found to be more efficient in pathogen removal (Total Coliforms, Shigella spp., Salmonella spp., Pseudomonas spp., Vibrio spp., Enterococcus faecalis) 7.04 ± 0.17, 6.53 ± 0.08, 4.0 ± 0.42, 7.67 ± 0.08, 5.73 ± 0.70 and10 5.23 ± 0.96 Log10 reductions respectively compared to SSVFCW (5.28 ± 0.18, 5.18 ± 0.09, 3.74 ± 0.74, 6.98 ± 0.01, 3.97 ±0.32, 4.74 ± 1.08 Log10 reductions respectively) and SSSVFCW (4.48 ± 0.46, 4.83 ± 0.15, 2.74 ± 0.44, 6.71 ± 0.03, 4.31 ± 0.49, 5.03 ± 1.20 Log10 decreases respectively). For abiotic factors (Chemical oxygen demand, total Kjeldahl nitrogen, and phosphorus) also TSVFW shows better efficiency (45 ± 8.7, 24.7±4.5 and 3.1, ± 0.2 g.m-2, respectively) than SSVFCW (12 ± 1.3, 7.6 ± 0.4 and 1.8 ± 0.2 g.m-2 respectively) and SSVFCW (6.3 ± 1.1, 7.7 ± 0.1 and 1.2 ± 0.1 g.m-2 respectively). However, the removal efficiency of both single-stage wetlands was comparable.

With India’s population growing at a rapid pace, traditional agriculture will have a tough time meeting future food demands. Water availability and conservation are major concerns for farmers. This paper aims to discuss the aspects related to designing and fabricating an automatic irrigation system using the Internet of Things (IoT) which will save the farmer’s time and money significantly. Human intervention in fields will be reduced. Changes in soil moisture are detected by soil moisture sensors and irrigation is automated using IoT. The proposed system is most economical for underdeveloped places because it is very cost-effective. Based on the soil moisture content, the sensor detects and sends signals to the node MCU, which activates the motor. When the plants receive enough water, the motor automatically shuts off. The user will be alerted about the soil’s moisture content through his mobile phone. The proposed smart irrigation system is implemented at our campus which conserves energy and water.

The pellets of waste produced by Society Electric [Listrik Kerakyatan (LK) 2] at the STT-PLN have not been efficiently utilized in terms of energy. The STT-PLN canteen consists of 14 stalls with an installed electricity capacity of 1300 VA, each with 12h of use/day. This study aimed to convert LK 2 waste pellets into electrical energy to supply electricity to the STT-PLN canteen. This research method uses quantitative methods, i.e., by calculating the amount of energy produced adjusted to the needs of the canteen. Gasification technology was chosen due to its high efficiency and lower emission impacts in the waste combustion technique. Based on the analysis, the gasifier engine that complies with this requirement was TG30-1 with a maximum capacity of 25 kVA and requires a flow rate of 10 kg.h-1 of waste pellets. The amount of waste pellets used for this plan was 120 kg.day-1. The assessment results of this plan indicated a net present value of IDR 302,218,609.33, an internal rate of return of 25.7983%, and a PBP of 5.66 years. Based on the economic analysis, the establishment of plants for the conversion of waste to power was declared feasible to operate.

Localized corrosion is a serious, hazardous destroyer of steel petroleum pipelines meant for long-time use. However, previous studies on localized corrosion primarily focused on local corrosion morphology and corrosion rate of bulk metals because detecting the corrosion state of occlusive metals is difficult. Herein, we employ a simulating occluded battery unit to disclose the local corrosion behavior of the steel petroleum pipeline (N80 steel) in an occlusive S2–-enriched solution. After simulating localized corrosion in the S2–- containing corrosion solution using the occluded battery unit, the occlusive solution was acidified and the migration amount of S2– to the occluded area increased. Despite the increase of S2– concentration, the addition of quinoline corrosion inhibitor (0.8 wt%) still effectively impedes the corrosion of the occluded metal. Moderately raising the environmental temperature can stimulate the activity of the inhibitor and promote the inhibition effect. The quinoline corrosion inhibitor displays the maximum inhibition rate at an elevated temperature of 50°C. Meanwhile, a maximum over the temperature of 60°C-70°C will likely accelerate the failure of the inhibitor.