Campus 2 of the National Institute of Technology (ITN) Malang shows its commitment to utilizing solar energy by adopting a 500 kWp photovoltaic solar power plant (PV), making it the largest in Indonesia for a private university. This research aims to evaluate the economic feasibility of photovoltaic solar power plants (PV) at Campus 2 of the National Institute of Technology Malang. The implementation of renewable energy, particularly photovoltaic solar power, is gaining attention due to its contribution to reducing greenhouse gas emissions and economic growth. However, the development of renewable energy sources faces several challenges, including the limitations of economic feasibility studies in Indonesia. A mixed-methods research approach is used, combining qualitative and quantitative data. Qualitative data are obtained from interviews with PV management staff, while quantitative data include net present value (NPV) calculations and payback periods (PBP). The research findings indicate that the on-grid photovoltaic solar power plant at Campus 2 of the National Institute of Technology (ITN) Malang has a capacity of 500 kWp, with a peak load reaching 380 kVA. The total project cost is Rp. 4,084,498,826, with annual operational and maintenance costs of Rp. 81,595,607. The price of electricity from the on-grid photovoltaic solar power plant is Rp. 930 per kWh. An NPV value of Rp. 7,789,395,602 indicates future profitability, while a PBP of 8.55 years demonstrates feasibility in terms of return on investment. In conclusion, the on-grid photovoltaic solar power plant at Campus 2 of the National Institute of Technology Malang has good economic feasibility due to factors such as controlled costs, competitive prices, a positive NPV, and a short PBP. Regular evaluations are necessary to ensure efficient operation and maximum benefits.

This study was conducted to determine strategies to enhance the sustainable forest management practices of the Tagbanua tribe. Specifically to describe the biodiversity and soil characteristics of the ancestral domain. The modified belt-transect method for biodiversity assessment developed by B+WISER (2014), further modified by the Department of Environment and Natural Resources (DENR) in the assessment, was used in this study. Results of soil chemical analysis showed significant variations among various land uses. The ancestral domain had at least 73 plant species belonging to 34 families and 59 genera. Four (4) taxa whose SN/families were still undetermined and another three (3) genera under families Annonaceae, Meliaceae, and Sapindaceae were unidentified. It had 12 plant species that are threatened with one critically endangered based on the list of threatened Philippine plants of the DENR. On the other hand, a total of 372 birds representing 61 species from 29 families were recorded. The high Shannon-Weiner Diversity Index (H’=3.69) and Shannon’s Evenness (HE=0.90) values indicate high avifaunal diversity and equitable distribution among the detected species. Most of the conservation priority species recorded in the area are Palawan endemic species. The survival of these birds is threatened by extinction due to habitat loss. This observation emphasized the importance of the ancestral domain as a refuge for these endemic species and conservation priority areas.

The majority of flood assessment and warning systems primarily focus on the occurrence of floods caused by river overflow, taking into account factors such as intense precipitation. Improving flood resilience, on the other hand, requires a deeper understanding of how these factors affect each other and how specific local conditions can have an impact. This study offers impartial tools for estimating the severity of the effects brought on by heavy rainfall to facilitate the prompt communication of effective measures, such as the evacuation of livestock and human settlements and the provision of medical assistance. These tools take into account the cascading effects of various causative factors contributing to heavy rainfall. This article aims to assess the various factors that contribute to the impacts of heavy rainfall, including the timestamp (indicating soil saturation and moisture levels), river gauges (determining water congestion in canal systems), average aerial precipitation (indicating runoff), and the rainfall itself, taking into account both in situ and ex-situ impacts. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes are some of the machine learning methods used in the study to find out how dynamically vulnerable affected districts are to flooding in different compound scenarios. This analysis is conducted by leveraging historical observed datasets. The results demonstrate the feasibility of mitigating the issue of excessive and insufficient flood warnings resulting from the cumulative effects of intense precipitation. By implementing a categorization system that divides the affected areas into various portions, or districts, according to the main factors contributing to flooding, namely rainfall, river discharge, and runoff, The suggested model presents novel insights into the sequential consequences of intense precipitation in the regularly inundated regions of North Bihar, India. Innovative tools can serve as valuable resources for flood forecasters and catastrophe managers to comprehend the extent of flooding and the consequential effects of intense precipitation.

The present study aims to characterize the water and sediment quality of the Colorado and Alajua rivers within Ecuador’s Ambato River watershed, with a specific focus on the presence of heavy metals. Measurements were conducted at five sampling points along the upper and lower zones of each river, where both physicochemical and microbiological parameters, as well as concentrations of heavy metals in water and sediments, were analyzed. Most parameters exhibited statistically significant differences, as determined by the analysis of variance (ANOVA), between the values observed in the upper and lower zones of the micro-watersheds. Water quality in the mentioned rivers was assessed using specific water quality indices, WQI, namely the NSF-WQI and Dinius WQI. Additionally, the impact of heavy metal presence in the water and sediments was evaluated using the Heavy Metal Evaluation Index (HEI). While most parameters met the Ecuadorian quality standards for water sources intended for human consumption, concerns emerged regarding elevated levels of total and fecal coliforms along both rivers, which could limit the suitability of these rivers as a water source for human use and consumption. At various sampling points, water quality criteria for the preservation of aquatic life were not met for several heavy metals. For example, the Colorado River exhibited elevated levels of zinc (59-76 μg.L-1), copper (12-47 μg.L-1), lead (1.2-3.9 μg.L-1 ), iron (0.33-0.37 mg.L-1 ), and manganese (0.37-0.47 mg.L-1), while the Alajua River showed excess copper (11 μg.L-1), iron (0.61-0.72 mg.L-1), and manganese (0.62-0.98 mg.L-1). Geological factors likely contribute to the concentration of heavy metals in the upper segments of the rivers, while agricultural runoff may contribute to concentrations in the lower segments. Sediments exhibited higher average values of the Heavy Metal Evaluation Index (HEI) (20.6-26.7) compared to water samples (13.9-15.4), indicating a potential accumulation of heavy metals in the river sediments. Overall, both rivers exhibited contamination levels ranging from regular to moderate, as indicated by the calculated average Water Quality Indices (WQI), with certain areas showing slight contamination or meeting acceptable standards. These results highlight the influence of anthropogenic activities on water quality, emphasizing the necessity of continuous monitoring to assess and control their impact.

The present study focuses on estimating the Trophic State Index (TSI) of Renuka Lake, the smallest Ramsar site in India, utilizing in-situ observed Secchi disk transparency (SDT) and satellite data. Site-specific algorithms were developed by establishing the relationship between the spectral band ratio of Landsat 8 OLI and LISS-III with that of in-situ measured SDT data. Notably, the exponential regression model outperformed other regression models (linear, logarithmic, polynomial, and power), achieving a better model output (R2=0.94). Additionally, water quality parameters, namely pH and dissolved oxygen (DO), were measured using the TROLL 9500 multi-parameter instrument. Various interpolation methods were applied to the in-situ data, with the exponential regression model yielding the most accurate results.This method was subsequently selected to generate two-dimensional water-quality images of Renuka Lake. The combined analysis of in-situ and satellite-derived trophic status indicates the eutrophic to hypereutrophic condition of the lake’s eastern and western parts. Satellite imagery spanning 2010-2019 consistently reveals a eutrophic state in the lake, with fluctuations in intensity over the period. The sustained eutrophic condition is attributed to escalating human-induced activities surrounding the lake, particularly in the western region.

The presence of Hydrogen sulfide, Methane, Volatile Organic Compounds (VOCs), and other odorous compounds in the ambient air is the root cause of the offensive odor emitting from the MSW dumping yard. Composition features and health risks associated with odor emissions concentrations in MSW dumping yards. This paper aims to provide an overview of research on health problems due to their composition, ecological impacts, and potential health risks of volatile organic compounds (VOCs) and to examine the relationship between VOC exposure and chronic illnesses in humans and the environment. In this study, a comprehensive investigation of VOC odor emission from an urban MSW dumping site has been performed. The VOC odor sample was analyzed using the GC-MS technique. The maximum VOCs concentration reported is due to tert - butylbenzene at 1.41μg.m-3 and the minimum is due to Sec-butylbenzene at 0.07 μg.m-3. Scientific databases, including Google Scholar, California Office of Environmental Health Hazard Assessment (OEHHA), and US EPA (Integrated Risk Information System (IRIS), were searched extensively using a bibliographic technique, in addition to a case study on MSW dumping yard workers. The findings of epidemiologic and experimental research, the emission of odors as a result of volatile organic compounds (VOCs) can cause a variety of non-cancerous health effects that are linked to abnormal functioning of the body’s vital organs, including the nervous and coronary, and pulmonary systems. It can also have minimal impact on the environment by causing global warming and ozone layer depletion. The odor emissions from the dumpsite pose both carcinogenic and noncarcinogenic risks to the health of the individuals participating in the dumping yard. As a result of these results, it is important to manage odor emissions (VOCs) during composting and take steps to reduce their negative effects on the environment and public health.

The residual dye within the soil from the synthetic dye manufacturing and fabric industries is a global state of affairs. The discharge consists of an excessive content of pigments and other components, creating complicated structures. It leads to damage to the soil structure and its fertility. Amid existing amputation methods, microbial remediation takes significant consideration owing to its subordinate charge, sophisticated proficiency, and fewer influences on the milieu. The current study was premeditated for the seclusion and portrayal of azo dye- dye-decolorizing bacteria, which is a criterion for emerging a microorganism-facilitated treatment of adulterating dyes. In this present investigation, twenty sorts of bacteria that were talented to decolorize seven kinds of azo dyes (Crystal Violet, Methylene Blue, Safranine, Congo Red, Methyl Orange, Malachite Green, and Carbol Fuchsin) were isolated from dye-polluted soil from the dying industry near the railway station; in Calicut. Based on 16S rDNA scrutiny, the most resourceful decolourizing bacteria for these azo dyes was identified as Priestia megaterium strain NRBC 15308. After characterization, Priestia megaterium was found to be optimally nurtured at 35°C, on a pH of 7, with a 1.5% glucose concentration in a minimal salt medium. 100% decolorization of a 6% dye solution was found at optimal conditions by Priestia megaterium. Priestia megaterium can decolorize cotton and gauze suspended in the dye solution in 24 hours. Bioremediation studies with the isolate proved that the inhibition effect of the dye solution on seed germination could be removed by the application of Prestia megaterium. The isolation of Priestia megaterium strain NRBC 15308 as a dye-degrading bacterium holds immense promise for remediating dye-contaminated soil.

Increasing concerns related to climate change and extensive use of water resources have depleted the available water for use. For water as an essential requirement for humans to carry onto their day-to-day chores, access and availability of water becomes the highest priority. Technology-based solutions support water generation, filtration, quality testing, water distribution, and many other areas. The present paper dwells on the user acceptance of these technologies. A conceptual model was developed through a literature review and named as Water-Technology Acceptance Model (W-TAM). The data was collected through a self-designed survey instrument to empirically test the proposed model. Analysis of this data was done with confirmatory factor analysis and structural equation modeling. It was observed that the actual use of these technologies depends on the ease of use and usefulness. Attitude to use them also matters. Although perceived risks and affordability did affect the use of W-TAM, trust, and regulatory aspects did not confirm their role in the adaptation of W-TAM. These findings will provide meaningful insights to the stakeholders and will help them in the practical implementation of these water-based technologies. This may also help service providers in the formulation of policies for technology-based water generation.

Due to rapid industrial growth and the increased economic status of people, water sources across the globe are being significantly polluted with a wide array of effluents. Industrial, agronomic, and customary activities have led to the repeated infestation of water by discarded materials. Consequently, there is an urgent need for advanced technologies to effectively eradicate these impurities from wastewater. Among the various methods established for wastewater remediation, the adsorption process has gained remarkable significance due to its efficiency and effectiveness. The use of nano adsorbents (NADs) represents an emerging solution to these environmental issues. NADs possess exceptional physical and chemical characteristics, which enhance their applicability compared to traditional adsorbents. Their advanced grade, prominence, and excellence in various arenas make them a superior choice for wastewater treatment. Recent explorations have shown that NADs, such as carbon nanotubes, graphene, and metal and metal oxide nano adsorbents, have a pronounced and favorable impact on wastewater treatment. The focus of this review article is to provide current data and insights into the use of NADs for wastewater remediation. It aims to highlight the benefits of these novel materials and to discuss the potential areas for further improvement in this field. By exploring the latest advancements and applications of NADs, this review seeks to contribute to the ongoing efforts to address the critical issue of water pollution and to promote sustainable water management practices.

Landslides are significant natural hazards that cause damage to the environment, life, and properties, mainly in hilly terrain. This research was mostly focused on generating a landslide susceptibility zone map of Papumpare District, Arunachal Pradesh, and classifying the region from high susceptibility to least susceptibility using AHP modeling techniques considering the landslide causative factors. The Analytical Hierarchy Process (AHP) is a multicriteria decision-making model (MCDM) in which each parameter is compared based on its role in triggering a landslide. A total of eight parameters were selected based on the factors that could affect the most, like Slope, Rainfall, Drainage Density, Lineament Density, Geomorphology, Soil, Geology, and Land use/Land cover. These layers were prepared using ArcGIS 10.8 software and ERDAS IMAGINE 2014. Based on the output, the region was classified into five zones of landslide susceptibility classes. Of these, the high-very-high landslides are mostly amassed near the steep and disturbed slopes due to earth-cutting, especially for building or construction of roads. Validation was done using the ROC curve (73.2%) suggesting good performance of the model. The outcome of this work will provide information for proper landslide hazard management and will help in formulating suitable mitigation strategies in the future.