This research aimed to identify the most appropriate probability distribution for modeling average monthly rainfall in the agro-climatic zones of West Bengal and to detect any trends in this data. The study utilized historical rainfall data spanning 51 years (1970-2020) obtained from the IMD in Pune. To determine the best-fitting distribution and assess trends, 23 different probability distributions were employed, with the Mann-Kendall test and Sen’s slope estimator used for trend analysis. Goodness-of-fit tests, including the Kolmogorov-Smirnov, Anderson-Darling, and Chi-square tests, were employed to determine the most suitable distribution. The findings indicated that the Generalized Extreme Value, Gamma, and Lognormal (3-parameter) distributions were the best fits for two specific districts. The monthly rainfall distributions can be effectively used for predicting future monthly rainfall events in the region. The Mann-Kendall test revealed an increasing trend in rainfall for Kalimpong and Nadia Districts and a decreasing trend for Malda District.

In the realm of precision agriculture, a pivotal challenge lies in the detection, identification, and grading of crop diseases. This multifaceted task necessitates the involvement of expert human resources and time-sensitive actions aimed at mitigating the risks of production losses and the rapid spread of diseases. The effectiveness of the majority of developed systems in this domain hinges on the quality of image features and disease segmentation accuracy. This paper presents a comprehensive research endeavor in the domain of Content-Based Image Retrieval (CBIR), specifically tailored to detect and classify leaf diseases. The proposed system integrates both color and texture features to underpin its functionality, providing a robust framework for accurate disease detection. By leveraging advanced image processing techniques, the system enhances the precision of disease identification, which is crucial for timely and effective intervention in agricultural practices. To evaluate the system’s performance, maize leaves afflicted by rust and blight serve as prime candidates for testing. These diseases were chosen due to their prevalence and significant impact on crop yield. The experimental results demonstrate that the developed system consistently excels in its disease detection and identification tasks, boasting an impressive efficiency rate of 98.33%. This high level of accuracy underscores the potential of the system to be a valuable tool in precision agriculture, aiding farmers and agricultural experts in maintaining healthy crops and optimizing production. The integration of color and texture features not only improves the detection accuracy but also provides a comprehensive understanding of the disease characteristics. This dual-feature approach ensures that the system can distinguish between different types of diseases with high precision, making it a versatile solution for various agricultural applications. The findings of this research highlight the importance of advanced image analysis techniques in enhancing the capabilities of disease detection systems, paving the way for more efficient and effective agricultural practices.

Prevention of the occurrence and development of emergencies of a natural and man-made nature is one of the basic fundamental foundations of ensuring the national security of any state. The most important mechanism for preventing emergencies is an effective system of monitoring and forecasting emergencies established at the state level. In the process of functioning such a system, one of the main urgent problems requiring constant attention, continuous research, system analysis, and the search for solutions by scientific methods and methods is to increase the reliability of emergency forecasts. In this format, special attention is currently being paid worldwide to a comprehensive assessment of the adverse consequences of emergency situations, primarily related to the safety of the population, environmental conservation, and environmental safety. From the standpoint of solving this significant scientific and practical problem, the purpose of this work was to develop and justify a more advanced method for calculating the feasibility of forecasts of emergencies with environmental consequences as a tool for a reasonable detailed assessment of the quality, optimality of emergency forecasting processes and the reliability of the forecasts themselves.

This study marks the first report on the genetic characterization of Penicillium expansum strain capable of mycotoxin production isolated from river water. Situated in Ganagalawanipeta village, Srikakulam, Andhra Pradesh, India, where river water serves as a vital resource, our investigation probed the presence of pathogenic opportunistic fungi adept at mycotoxin synthesis. Over six months, 30 samples were collected to assess their occurrence. This article revolves around the use of morphological traits for Penicillium genus identification. Precise species determination involved PCR analysis using universal primers ITS1 and ITS4, followed by sequence analysis through NCBI-BLASTn and the ITS2 database. The analysis indicated a striking 99.49% genetic similarity to Penicillium expansum isolate MW559596 from CSIR-National Institute of Oceanography, Goa, an Indian isolate, with a resultant 600-base pair fragment. This sequence was officially cataloged as OR536221 in the NCBI GenBank database. Sequence and phylogenetic assessments were conducted to pinpoint the strain and geographical origin. Notably, the ribosomal nuclear ITS region displayed significant inter- and intra-specific divergence, manifested in DNA barcodes and secondary structures established via minimum free energy calculations. These findings provide crucial insights into the genetic diversity and potential mycotoxin production of P. expansum isolates, shedding light on the environmental repercussions and health risks associated with river water contamination from agricultural and aquaculture effluents. This pioneering research advances our understanding of mycotoxin-producing fungi in aquatic environments and underscores the imperative need for water quality monitoring in regions reliant on such water sources for their sustenance and livelihoods.

Modeling the fate and transport of spills in rivers is critical for risk assessment and instantaneous spill response. In this research, a one-dimensional model for instantaneous spills in river systems was built by solving the advection-dispersion equation (ADE) numerically along with the shallow water equations (SWEs) within the MATLAB environment. To run the model, the Ohio River’s well-known accidental spill in 1988 was used as a field case study. The verification process revealed the model’s robustness with very low statistic errors. The mean absolute error (MAE) and root mean squared error (RMSE) relative to the absorbed record were 0.0626 ppm and 0.2255 ppm, respectively. Results showed the spill mass distribution is a function of the longitudinal dispersion coefficient and the mass decay rate. Increasing the longitudinal dispersion coefficient reduces the spill impact widely, for instance after four days from the mass spill the maximum concentration decreased from 0.846789 to 0.486623 ppm, and after five days it decreased from 0.332485 to 0.186094 ppm by increasing the coefficient from 15 to 175 m2/sec. A similar reduction was achieved by increasing the decay rate from 0.8 to 1.2 day-1 (from 0.846789 to 0.254274 ppm and from 0.332485 to 0.0662202 ppm after four and five days, respectively). Thus, field measurements of these two factors must be taken into account to know the spill fate in river systems.

Indiscriminate disposal of crop and animal wastes has grown in acceptance across the globe as an environmentally hazardous practice. This study used a 225L polyethylene digester that was specially made to produce biogas from anaerobic co-digestion of palm oil sludge, cassava peels, and cow dung using ground eggshells for pH stabilization and a greenhouse for temperature control. Cassava peels, palm oil sludge, cow dung, and water were combined in a ratio of 1:1:2:5.3, respectively, and 1.3 kilograms of crushed eggshells were added. The bio-digestion system generated 650.60 L of cumulative biogas throughout the 30-day sludge retention period. The pH averaged 6.0, and the slurry temperature averaged 34.76oC during digestion, which is favorable for the production of biogas since microbial populations thrive under hospitable conditions. The biogas produced after a hydraulic retention time (HRT) of over 20 days had the highest methane concentration of 60%, while days under 10 HRT had the lowest methane content of 45.5%. On the 13th day of anaerobic digestion, biogas output peaked at 34.90L, and pH and temperature were maintained at 6.5 and 35.0°C, respectively, the ideal ranges for a healthy process. An efficient technique for producing energy in the form of biogas was shown by optimized anaerobic co-digestion of animal and crop waste utilizing ground eggshells and a greenhouse for pH and temperature control. Future research should focus on developing more efficient, cheaper microbial agents, such as enzymes for biological pre-treatment of palm oil sludge to reduce lignin, which negatively impacts biogas generation.

This study examines the cultural livelihood of Kogi State and its effects on the environment. The study describes some of the cultural livelihood practices found in Kogi State, considering the contemporary condition of cultural livelihood and its effects on the environment. Secondary and primary data were employed, which include archives and internet search engines. Using a 4-stage sampling procedure, data were collected from a 120-person sample through an interview, field observation, a focus group discussion, and a questionnaire. Descriptive statistics using frequencies, percentages, and charts were used for the analyses. The results were compiled using the Statistical Package for Social Sciences (SPSS). Findings show that about 85% of the participants discovered crop farming, arable farming, weaving, blacksmithing, fishing, and festivals of harvest, such as the New Yam Festival, among others, as the predominant cultural livelihoods. The local farming implements were made of local materials, like stones and wood. They have indigenous crop production, protection, and harvest techniques. The farming tools were economical in terms of labor, affordability, and time savings in the subsistence farming system. The study discovered that cultural livelihoods are 4% very efficient and 56% on the verge of extinction. Analyses of the effect of cultural livelihood show that 78% have a high negative effect on the economic environment, 57% have a moderate negative effect on the social environment, 51% hurt the political environment, and 22% have a low negative effect on the political environment. The intervention of the various tiers of government with the cooperation of the various communities is needed for the provision of a conducive environment for the practice of cultural livelihood, particularly in the aspect of insecurity. Adequate provision of modern equipment, funding, and social welfare services is also recommended to enhance cultural livelihoods.

This research paper presents a comprehensive assessment of the built-up area in Mysuru City over the decade spanning from 2010 to 2020, employing advanced geospatial techniques. The study aims to analyze the spatiotemporal patterns of urban expansion, land-use dynamics, and associated factors influencing the city’s built environment. Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. The methodology involves the acquisition and preprocessing of multi-temporal satellite imagery to delineate and map the built-up areas at different time intervals. Land-use change detection techniques are employed to identify and quantify alterations in urban morphology over the specified period. Additionally, socio-economic and environmental variables are integrated into the analysis to discern the drivers of urban growth. The outcomes of this research contribute valuable insights into urbanization dynamics and land-use planning strategies, facilitating informed decision-making for sustainable urban development.

As part of green economics, taxes are imposed on emissions of pollutants that adversely impact the environment and public health to reward more innovative, environmentally sustainable, and low-carbon resource use. There are still many nation-states testing the concept of green taxation. Many environmental performance indicators place India low on the list of countries with the worst pollution. One of the main sources of pollution is vehicle exhaust. Green taxes will be imposed on older motor vehicles under guidelines released by the Indian government in 2021. The United Nations Framework Convention on Climate Change received the Indian Nationally Determined Contribution Report in 2022. Taxonomies and low-carbon transport systems were prioritized in India, and incentives and tax breaks were offered to encourage the manufacture and use of vehicles that consume more ethanol. Academic discussions and literature on the subject are still lacking among the masses. Researchers intend to analyze the legal and economic measures taken by the Indian Government to curb vehicular pollution against this background. Due to its significant contribution to air and water pollution, as well as greenhouse gas emissions, the automobile industry has come under increasing scrutiny in recent years. India and China, for instance, have implemented green tax policies to reduce the automotive sector’s environmental footprint and promote environmental sustainability. These policies are effective, but not all of them address the disproportionate impact of environmental injustice on vulnerable populations. Specifically, this study examines the impact of Indian green tax policies on environmental justice in the automobile industry as compared to those in China. A key aim of this study is to provide insights into the strengths and weaknesses of the green taxation policies adopted by each country in the automotive sector, as well as their implications for achieving environmental justice, by analyzing the scope, enforcement, impact on vulnerable communities, industry implications, and alignment with international commitments.

This study aims to design a community-based plastic waste management model specifically for Bangun Village, Mojokerto. Using a qualitative approach through a detailed case study, we gathered rich data from observations, interviews, and document reviews. Our findings reveal that the plastic waste management situation in Bangun Village is fraught with significant social, economic, and environmental challenges. These include inadequate waste segregation, limited recycling facilities, and a general lack of community awareness and participation. The proposed model seeks to address these issues by implementing several key components: community-based plastic waste collection and processing, educational programs to raise awareness and promote sustainable practices, partnerships with external stakeholders such as local government bodies, NGOs, and private sector entities, and institutional restructuring to support and sustain these initiatives. Central to this model is the belief that community education and awareness are crucial foundations for fostering sustainable behavior. By actively involving the community in the waste management process, the model not only aims to mitigate the plastic waste problem but also seeks to provide economic and social benefits to the residents of Bangun Village. This includes creating job opportunities, improving public health, and enhancing the overall quality of life. The strength of this model lies in its ability to integrate community participation, policy support, and external partnerships, making it a robust and effective solution for sustainable plastic waste management. By fostering a collaborative and inclusive approach, the model aims to create a sustainable and resilient community that can effectively tackle the plastic waste challenge while reaping economic and social benefits. In conclusion, the community-based plastic waste management model proposed for Bangun Village has the potential to bring about significant positive changes in the way plastic waste is managed. Through this model, we hope to empower the community to contribute to solving the plastic waste problem while also benefiting economically and socially.