This study investigates the physical characteristics of red clay using the IDW approach and linear regression modeling in an area of 268.12 km2, focusing on Kirkuk, Bor, and Jambor structures. Through the analysis of 52 soil samples and the integration of laboratory data with IDW and regression results, several significant findings have emerged. The IDW method combined with linear regression proves to be a cost-effective and efficient approach for obtaining soil property data and generating accurate digital maps of red clay’s physical features. The Silt concentration exhibits a wide range, while the gravel content remains relatively low, indicating the predominance of silt in the soil composition. Analysis of Atterberg limits reveals the soil’s behavior and consistency in response to moisture, with the plasticity index generally falling within the low to medium range due to the considerable silt content in most soil samples. The linear regression model highlights positive correlations between the liquid limit, plastic limit, and plasticity index. Moderately positive relationships exist between the liquid limit and clay content, as well as a weak positive association between the liquid limit and specific gravity. Dry density, on the other hand, shows no significant correlation with other physical variables, suggesting its independence from the measured parameters. The plastic limit demonstrates a stronger relationship with the clay content compared to the liquid limit. Additionally, weak positive correlations are found between the liquid limit, plastic limit, and specific gravity and water content, indicating the influence of moisture on these parameters. Furthermore, gravel exhibits a moderate positive correlation with sand and silt concentrations, while a strong positive correlation is observed between sand and silt contents, underscoring their close association with the soil composition.

The present work studied the effects of alkali pretreatment on the cellulosic biomass of rice straw. The improvement in the cellulose content and reduction in the lignin and hemicellulose percentage was observed with alkali pretreatment. Fourier transformation infrared spectroscopy (FTIR) and Scanning electron microscopy (SEM) analysis confirm the modification in the surface structure of alkali rice straw. Further, the study investigated the potential of different types of seed sludge as inoculum sources for dark fermentative biohydrogen production. In comparison to other sludge samples (beverage industry, food industry, and sewage treatment plant sludge), the mixed culture of sewage treatment plant sludge had the highest cumulative volume of biohydrogen (90.52 mL), as well as the highest hydrogen production yield (0.75 moleH2/mole) with the substrate utilization of 86.72%. The results provide information on the best sludge source for enhancing biohydrogen production in the dark fermentation method.

The rapid development of industry has led to the generation of a large amount of electroplating wastewater. The direct discharge of untreated electroplating wastewater may lead to the formation of toxic metal-organic complexes, which is a challenging problem for human health and the living environment of organisms. Due to the high solubility of heavy metals in aquatic environments and their easy absorption by organisms, effective treatment of electroplating wastewater is of great significance. The ultimate goal of electroplating wastewater treatment should be to recover metals and water from electroplating wastewater. In indoor experiments, pilot tests, and industrial applications of electroplating wastewater treatment, membrane treatment technology commonly used in wastewater terminal treatment has attracted great attention. Membrane treatment technology seems to be the most promising method for removing heavy metals and organic pollutants from electroplating wastewater. This article reviews the membrane treatment technologies for electroplating wastewater, introduces the advantages and disadvantages of various membranes in the treatment of electroplating wastewater, the removal efficiency of pollutant types, and their comparison. The focus is on the treatment effects of nano-filtration membrane, ultra-filtration membrane, micro-filtration membrane, reverse osmosis membrane, ceramic membrane, biofilm, etc., on electroplating wastewater. Compared with a single treatment method, the combination of different processes shows higher efficiency in removing various pollutants.

The growing population and rapid urbanization are significant challenges for Indian cities. Pune City generates nearly 2,258 tonnes of waste per day. Pune’s informal waste sector has demonstrated remarkable efficiency, cost-effectiveness, and self-sustainability. Moreover, it contributes to favorable economic and social outcomes for the city. With the support of the self-help group SWaCH Seva Sahakari Sanstha Maryadit, Pune, the municipal solid waste management model has successfully achieved a remarkable 95 percent segregation rate. Implementing the Pune municipal solid waste management model showcases the active and efficient engagement of informal waste workers in the collection and resource utilization process. This underscores the possibility of favorable economic, social, and environmental results stemming from collaborations between municipalities and waste pickers. This paper looks at the role of SWaCH in line with Pune Municipal Corporation towards the present waste management system. Primarily reliant on labor, this model accomplishes recycling tasks at a notably lower cost compared to conventional mechanized and centralized waste management approaches. It can also accomplish high recycling levels and relatively considerable plastic waste segregation. Promoting the retrieval of valuable materials, especially plastics, for local and global recycling enterprises actively contributes to the advancement of a circular urban waste management approach. The objective of this research is to explore and provide a realistic understanding of Pune’s current status of waste generation, collection, transportation, and disposal. Apart from the SwaCH-PMC model, the paper also focuses on plastic waste recycling, the Red Dot Campaign towards sanitary waste, and household e-waste management in Pune.

Diseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.

Dual concerns involving the rise in airborne pollutant levels and bulging need to protect-preserve human health have propelled the search for innovative means for air quality monitoring to aid in evidence-based decision-making (pollution prevention-mitigation). In this regard, moss bags have gathered a great deal of attention as active biomonitors. In this reflective discourse, we systematically review the world literature to present a bird’s eye view of moss bag applications and advances while highlighting potential concerns. We begin with a brief note on mosses as biomonitors, highlighting the advantages of moss bags over the passive technique (native moss), other living organisms (lichens, vascular plants), and instrument-based measurements. A major strand of moss bag research involves urban ecosystem sustainability studies (e.g., street tunnels and canyons, parks), while others include event-specific monitoring and change detection (e.g., SARS-CoV-2 Lockdown), indoor-outdoor air quality assessment, and change detection in land use patterns. Recent advances include biomagnetic studies, radioisotopic investigations, and mobile applications. Efforts are currently underway to couple moss bag results with a suite of indicators [e.g., relative accumulation factor (RAF), contamination factor (CF), pollution load index (PLI), enrichment factor (EF)] and spatially map the results for holistic appraisal of environmental quality (hot spot detection). However, while moss bag innovations and applications continue to grow over time, we point to fundamental concerns/uncertainties (e.g., lack of concordance in operational procedures and parameterization, ideal species selection, moss vitality) that still need to be addressed by targeted case studies, before the moss results could be considered in regulatory interventions.

The existence of a coal mining company in the vicinity of the community is something to be feared related to environmental damage due to coal mining. On the other hand, coal mining can have a positive impact on the economy of communities around the mine through corporate social responsibility programs. The problem in this research is that MSMEs need help to improve their performance. Therefore, this research aims to examine how the role of mining companies through corporate social responsibility (CSR) programs can contribute to the development of MSMEs in communities around mining areas. The company provides promotional assistance, funding, and capacity building. This research conducted surveys and interviews with respondents, namely MSMEs, around mining locations. The findings show that corporate social responsibility programs in coal mining companies have a positive impact on empowering MSMEs in communities around the mine. By providing training and promotion facilities to MSMEs, mining companies can also improve MSME performance compared to providing access to financial assistance programs. The company not only takes advantage of mining and focuses on its environmental impact but also the company’s role in empowering MSMEs.

SCS-curve number (CN) is one of the most well-liked and commonly applied methods for estimating surface runoff. The present study aims to calculate surface runoff using SCS-CN watershed-based calculation and geospatial technology in the Kurumballi sub-watershed Shivamogga District of Karnataka, India. The study area covers about an area of 47.67 sq. km. The union of land use/land cover classification with hydrological soil groups (HSG) yields the runoff estimation by the SCS-CN curve approach. This method calculates the runoff volume from the land surface flows into the river or streams. Moreover, the study area’s delineation of runoff potential zones was done using the thematic integration method. Different thematic layers were used, including lithology, geomorphology, soil, slope, land use and land cover, drainage, surface water bodies, groundwater contour, and isohyetal maps. Furthermore, associating it with the SCS-CN technique, the total surface runoff volume of the study area was estimated. The total surface runoff volume in the study area is 21065849.7 m3. To this study, thematic integration with the SCS-CN approach to estimate runoff for watersheds is valuable for improving water management and soil conservation.

Sawdust, a product of the forest industry is mostly left untreated as solid waste. This phenomenon is well observed along the Lagos Lagoon in Nigeria where hundreds of trees are cut daily by sawmills to deliver wood for mainly the furniture industry. Different types of trees are utilized in this manner and the massive amounts of sawdust produced as a result of these activities are polluting the environment causing health risks for humans and animals. Cellulose, a glucose bio-polymer is a major structural component of sawdust and could be developed as a renewable energy resource should the cellulose be degraded into glucose, a fermentable sugar. This saccharification was done with Aspergillus niger cellulase and to make the cellulose more susceptible for cellulase action the sawdust was delignified with hydrogen peroxide. Both delignified and non-delignified sawdust were treated with the cellulase enzyme at incubation temperatures of 30°C, 40°C, 50°C, and 60°C. Delignification proved to be effective as an increased amount of sugar was released from all delignified sawdust materials relative to the non-delignified materials when saccharified with A. niger cellulase. Most of the materials were degraded at an incubation temperature of 40°C and 50°C and the highest percentage saccharification of 58% was obtained during the degradation of delignifed cellulose from the tree, Ricindendron heudelotti

The study presented aims to find the most appropriate climate dataset for the data-scarce Jalaur River Basin (JRB), Iloilo, Philippines, by evaluating the statistical performance of five rainfall datasets (APHRODITE, CPC NOAA, ERA5, SA-OBS, and PGF-V3) with resolutions of 0.25° and 0.5° having a time domain of 1981 to 2005. Bilinear interpolation implemented through Climate Data Operator (CDO) was used to extract and process grid climate datasets with Linear scaling as bias correction to minimize product simulation uncertainties. The datasets were compared to the lone meteorological station nearest to JRB investigated at monthly and annual timescales using six statistical metrics, namely, Pearson’s correlation coefficient (r), coefficient of determination (R2), modified index of agreement (d1), Kling-Gupta efficiency, Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results indicate a strong positive correlation with the observed data for both rainfall and temperature (r > 0.8; R2, d1 > 0.80). Although graphical observation shows an underestimation of rainfall, goodness-of-fit values indicate very good model performance (NSE, KGE > 0.75; RSR < 0.50). In terms of temperature, variable responses are observed with significant overestimation for maximum temperature and underestimation for minimum temperature. SA-OBS proved to be the best-performing dataset, followed by ERA5 and PGF-V3. These key findings supply useful information in deciding the most appropriate gridded climate dataset for hydrometeorological investigation in the JRB and could enhance the regional representation of global datasets.