This study focuses on atmospheric aerosols, especially aerosol optical depth (AOD), over Dire Dawa, Ethiopia, from 2009 to 2020. At first, a correlation between the four satellite sensors and AERONET was made for validation purposes and to determine the sensor that best represents Dire Dawa. Intercomparisons were also made among the four satellite sensors. After all statistical tests, annual, seasonal, and decadal trend analyses were made. The validation results indicated that the AOD of MODIS-terra showed the best correlation with AERONET with R2 (0.78), RMSE (0.03), and MBE of 0.02 and represented the area better than the rest. The inter-comparison of AOD retrieved from multi-spectral satellite sensors showed a positive and satisfactory correlation between MODIS-Terra and OMI. Only MODIS-Aqua showed a linearly increasing mean annual AOD with R2 = 0.43. In three seasons (summer, autumn, and spring), AOD showed linear increments over the 12 years, with R2 ranging between 0.3 and 0.5. The three seasons also had nearly identical AODs of 0.23-0.28. However, winter had the lowest value of 0.2. MODIS-terra, out of the four sensors, exhibited increasing decadal tendency over the 2009-2020 period. Monthly analysis revealed that August had the highest AOD (0.265), and January had the lowest (0.14). The value of AOD obtained from this study over Dire Dawa shows a higher value during all seasons except during winter. Thus, this study gives a glimpse into the use of multi-spectral satellite sensors to monitor air quality over a semi-arid urban region.

The stated goal of the research is to investigate the surface water quality of the Baitarani River in Odisha to ascertain its compatibility for various uses. Large, complex datasets generated during the one-year (2021-2022) monitoring program were collected from 13 locations and encompassed 22 parameters. To examine temporal and spatial fluctuations in and to interpret these datasets, MCDMs like TOPSIS and the Entropy-based Water Quality Index (EWQI) were utilized. The physical and chemical outcomes of the current experiment were compared to WHO standards. According to the analysis’s results, turbidity and total coliform (TC) are indicators that have a greater impact on water quality in all locations during both seasons and are directly linked to home and agricultural non-point source pollution. As per EWQI interpretation, 30.77 % of the observations in PRM and POM fall under the poor category. The findings showed how anthropogenic activities have harmed St. 8, 11, 12, and 13 and require effective management. A quantifiable approach was also carried out to decide the efficacy of TOPSIS. Farming attributes, including SAR, % Na, RSC, MR, KI, and PI, were estimated to delineate the agriculturally practicable zones. This work can offer a reference database for the betterment of water quality.

Effective stormwater management can be used to regulate water quantity and quality for environmental sustainability, flood control, pollution reduction and other advantages of civil engineering infrastructures. Pollution of the environment and contamination of water sources can emanate from improper stormwater management. This study used a small-scale model of rainwater harvesting to analyze the design and model of urban stormwater management and treatment infrastructure for the neighborhoods in Abuja. The water quality of the treated stormwater retrieved has improved as a result of the usage of memory foam, alum, and chlorine to filter out contaminants and pathogens. With the fictitious stormwater treatment model created for this study, average values of the physicochemical parameters were collected from the stormwater discharge after it had been filtered and treated. The use of potash alum has had a variety of effects on the water’s quality. From 697 mg.L-1 to 635 mg.L-1, the total dissolved solids dropped. The DO dropped from 5.87 mg.L-1 to 3.92 mg.L-1 as well. Additionally, the turbidity rose from 4.42 FNU to 4.58 FNU, and the salinity rose from 0.7 PSU to 1.44 PSU, respectively. pH decreases from 19.78 to 15.17 mg.L-1, BOD decreases from 8.35 to 6.51, and COD decreases from 2.55 to 1.9. Calcium hardness has decreased from 287 mg.L-1 to 265.83 mg.L-1. The conductivity increases marginally from 3.24 ms.cm-1 to 3.82 ms.cm-1. The Fe2+ and Zn2+ ions exhibit a little decrease from 0.143 mg.L-1 to 0.055 mg.L-1 and from 0.092 mg.L-1 to 0.045 mg.L-1, respectively. Due to inadequate or nonexistent drainage systems in the many states and villages throughout the country, stormwater run-off management and treatment in Nigeria have been a colossal failure. Effective stormwater management can be sustained by using legal and environmental laws.

Glyphosate is a herbicide of a wide spectrum that alters the production of amino acids in plants, leading to their death. Due to its properties, it is used to eliminate weeds that interfere with human activity. The intensive use of this herbicide in the past decades has led to its frequent encounter in the environment as it has been detected in water, animals, and food destined for human consumption. Its impact on human health and the rest of living organisms has not been fully explored, given that many authors enter into contradictions with one another, specifically surrounding the role of surfactants in the commercial presentation of herbicides. The use of pesticides can have significant impacts on ecosystems, threatening bio-cultural diversity due to genetic contamination from transgenic crops. The effectiveness of Glyphosate-based herbicides in weed control is diminishing due to weed tolerance. However, the use of herbicides remains prevalent in large-scale crops due to the challenges of organic food production. In addition, the probable conflict of interest by the agrochemical industry does not bring a full picture with respect to the actions that world governments should take. Banning GLP-based herbicides may lead to the use of other pesticides, in which the long-term impacts will require further studies. The motivation for this research is the review of the latest advances of glyphosate in the world, considering the use and prohibitions of this herbicide, its interaction with water and soil, as well as the effects on both the environment and health. The search for information for this paper was carried out in the Mendeley, Elsevier, and Springer databases by filtering by the suitable keywords.

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.

To increase productivity and avoid waste, the construction industry has started implementing Lean ideas and methodologies in construction projects. Due to a lack of awareness of lean practices in the preparation, design, and execution of building and infrastructure projects, lean practices are not very familiar among construction projects, which are most commonly used in the manufacturing industry. Hence, an effort has been made in this paper to provide a comprehensive review of the literature and case studies to analyze the suitability of lean practice in sustainable waste management, increased productivity, and on-time project delivery. It aims to explore the effect of improving communication and fostering collaboration among stakeholders on time, costs, and resource management. The review identified the most commonly applied lean practices, Just in Time (JIT) and Last Planner System (LPS), and linked the adoption of lean techniques within the construction sector to a total of sixteen distinct benefits for the economy, society, and the environment. According to this study, lean techniques have a strong chance of boosting productivity in the construction industry and developing a sustainable built environment, but they also need to be used widely and continuously to achieve these goals.

Cryptocurrency has seen an increased popularity with the introduction of Bitcoins. It has been adapted in several countries and has become an alternate solution to conventional currency. Despite its benefits, some controversies surround the manufacturing of bitcoins. While all the countries are moving to sustainability development and global warming control, Bitcoin production has raised several concerns about environmental pollution and sustainability. The increased carbon emissions and high electrical consumption have accompanied the popularity of cryptocurrency. Hence, there is an immediate need to reduce the carbon footprint and electricity consumption caused by human cryptocurrency for a sustainable future. This study presents the current scenario and trends of worldwide cryptocurrency growth and discusses the environmental impact of cryptocurrency mining. It explores crypto mining worldwide and provides a qualitative review. Further, this article highlights the need to take necessary measures to control cryptocurrency circulation.

Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, an IoT-based Artificial Intelligence (AI) technique for forest fire detection has been proposed. This system leverages the Internet of Things (IoT) to collect real-time data from various sensors deployed in forest areas, providing continuous monitoring and early warning capabilities. Several researchers have contributed different techniques to predict forest fires at various remote locations, highlighting the importance of innovative approaches in this field. The proposed work involves object detection, which is facilitated by EfficientDet, a state-of-the-art object detection model known for its accuracy and efficiency. EfficientDet enables the system to accurately identify potential fire outbreaks by analyzing visual data from the sensors. To facilitate efficient detection at the outbreak of forest fires, a bi-directional gated recurrent neural network (Bi-GRU-NN) is needed. This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. These results demonstrate the effectiveness of the integrated AI and optimization techniques in enhancing forest fire detection and prevention. The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.

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.

Indonesia must establish a policy on the application of technology for mitigating greenhouse gas emissions because it is the nation that produces the most palm oil. When evaluating different technologies, policymakers should consider how much the technology will cost compared to the potential emissions abated, in terms of marginal abatement cost (MAC), which reflects priorities in the form of marginal abatement cost curves (MACC). The objective of this research is to evaluate and estimate the ranking of MAC from eight mitigation technologies used in Indonesia’s palm oil sector between 2020 and 2030. The least MAC is given as technology ranked first, namely the high-capacity boiler, with a value of $-19.61/tonne CO2eq followed by the high-efficiency steam turbine with $-7.2/tonne CO2eq, and the POME-to-biogas technology with $-0.1/tonne CO2eq. Additionally, the MAC of five additional technologies is positive, suggesting that implementation expenses were incurred. Subsequently, a sensitivity analysis is performed to see which technology ranks are impacted by interest rate fluctuations. Biogas upgrading technology is therefore liable to changes in the discount rate, which occur at different values. Other mitigation technologies, however, are also increasing their parameters, although less significantly than biogas upgrading, therefore this has no bearing on mitigation technology ranking.