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.

Precipitation is a fundamental variable that is widely used in the organization of water resources and has a great influence on hydrological processes and ecological assessment. This study investigated the quantitative effect of monthly precipitation on surface water area (denoted by the Modified Normalized Difference Water Index, MNDWI), vegetation area (denoted by Normalized Difference Vegetation Index, NDVI), and potential evapotranspiration (PET) during two years (2018 and 2021) in the city of Baghdad, Iraq. Using the Thornthwaite aridity index, the annual aridity was first assessed to quantify the climate category of these years. The result shows that they were semi-arid and very arid, respectively. The empirical relationships between precipitation and areas of MNDWI and NDVI, and between rainfall and PET, were also examined. Due to less precipitation in 2021, no relationship was found in arid climates, while in 2018 for semi-arid climates, precipitation had a positive non-linear correlation with MNDWI and NDVI areas and a negative correlation with PET.

Drought presents a substantial threat to both ecological and agricultural systems. Agriculture in Iraq is predicated on precipitation, which is a major contributor to the likelihood of drought resulting from even marginal fluctuations in precipitation. Furthermore, research suggests that Iraq suffers an approximate annual loss of 100,000 acres of arable land due to drought. NDVI and VCI, two significant indices, were utilized in this research to assess and monitor the severity of the drought in the northern region of Wasit province in Iraq. For the period from 1993 to 2023, drought intensity maps were generated utilizing NDVI-based VCI and the Geographic Information System (GIS), an extremely effective spatial data management instrument. NDVI results evidenced that the vegetation cover area was the highest in 1993 and 1998 and declined until it reached the lowest levels in 2023. The vegetation area was concentrated in the southwest parts. In contrast, VCI results demonstrated the extreme drought through the years from 2003 to 2023, which can be attributed to higher temperatures, evaporation, and lower amounts of rainfall. Throughout the thirty-year analysis period, extreme drought conditions were prevalent, especially in the last two decades. Furthermore, this drought should prompt the government to implement preventative measures to avert it. Implementing soil and water conservation measures, such as the establishment of percolation basins, contour bunds, and check dams, can also enhance drought management.

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.

Generally, in India, determining the chlorine and coagulant dosage in a WTP depends on the proficiency of operators, which may lead to overdosing or underdosing of coagulants and chlorine. Nevertheless, the determination of both coagulant and chlorine dosages frequently changes as inlet water quality varies which demands extensive laboratory analyses, leading to prolonged experimentation periods in water treatment plants. So objective of the study is to develop the precise relationship between coagulant dose and chlorine dose in a water treatment plant by using an artificial neural network (ANN). As a result, ANN models were developed to predict chlorine dose using coagulant dose by comparing the performance of the number of ANN models. It has been found that radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN) modeling provide better prediction. In RBFNN and GRNN modeling, the spread factor is varied from 0.1 to 15 to establish a stable and accurate model with high predictive accuracy. It is observed that the RBFNN model showed good prediction (R2 = 0.999). The application of a soft computing model for defining doses of coagulant and chlorine that are inextricably linked at a Water treatment plant (WTP) will be highly beneficial for WTP Managers.

The current research aims to optimize the solvent-based oil extraction process from Mahua (Madhuca indica) seed using response surface methodology and biodiesel production using heterogeneous catalysts. The oil extraction was varied through the levels of process parameters including extraction temperature (60 to 80°C), solvent-to-seed ratio (3 to 9 wt/wt), and time (2 to 4 h). The experiments were designed following the Central Composite model. The regression model provided optimal values for the selected process parameters based on the extraction yield percentage. To ensure the model’s reliability, it was experimentally validated. Maximum experimental oil yields of 50.9% were obtained at an optimized extraction scenario of 70 °C extraction temperature, solvent-to-seed ratio of 6 wt/wt, and time 4 h. The extracted oil’s physicochemical properties and fatty acid composition were tested. Also, using copper-coated dolomite as a catalyst, the extracted oil was transformed into biodiesel via transesterification. The FAME (94.31%) content of the prepared biodiesel was determined via gas chromatography. As a result, the findings of this study will be useful in further research into the use of Madhuca indica as a potential feedstock for biodiesel production.

This confirmatory study focused on studying the attitude and behavior as well as environmental awareness towards sustainable consumerism. The study also aimed to check if accountability on the part of brands and the government could enhance sustainability in the apparel industry. An empirical inquiry was conducted with 396 respondents, considering they are consumers with purchasing power. The collected data were analyzed using correlation and descriptive analysis. Based on the findings, consumers’ apparel use and brand accountability are positively associated. At the same time, it was found that the attitude and behavior of consumers are the least essential determinants for sustainable apparel consumption. This might imply that their optimistic outlook may not always translate into real purchase behavior, which is consistent with earlier studies. The results of this research provide a foundation for a better comprehension of the many factors, including the sustainability of a clothing brand or product, which may affect consumer behavior. This approach could help the fashion industry develop practical strategies and alter how people think about and utilize apparel in the future.

Smart grids are modernized, intelligent electricity distribution systems that integrate information and communication technologies to improve the efficiency, reliability, and sustainability of the electricity network. However, existing smart grids only integrate renewable energies when it comes to active demand management without taking into consideration the reduction of greenhouse gas emissions. This paper addresses this problem by forecasting CO2 emissions based on electricity consumption, making it possible to transition to renewable energies and thereby reduce CO2 emissions generated by fossil fuels. This approach contributes to the mitigation of climate change and the preservation of air quality, both of which are essential for a healthy and sustainable environment. To achieve this goal, we propose a transformer-based encoder architecture for load forecasting by modifying the transformer workflow and designing a novel technique for handling contextual features. The proposed solution is tested on real electricity consumption data over a long period. Results show that the proposed approach successfully handles time series data to detect future CO2 emissions excess and outperforms state-of-the-art techniques.

Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. GML emerges as a potent and indispensable tool, adeptly leveraging the intrinsic interconnectedness embedded within environmental datasets. Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. This article does not merely scratch the surface of GML’s applications but dives deep into its tangible implementations, unraveling its potential to revolutionize environmental science and pollution technology. It endeavors to bridge the gap between theory and practice, weaving together relevant ecological theories and empirical evidence that underpin the theoretical foundations supporting GML’s practical utility in environmental domains. By synthesizing theoretical insights with real-world applications, this research elucidates the profound transformative potential of GML, paving the way for proactive and data-driven approaches toward addressing pressing environmental challenges. In essence, this harmonization of theory and application catalyzes advancing the adoption of GML in environmental science and pollution technology. It not only illuminates the path towards sustainable practices but also lays the groundwork for fostering a holistic understanding of our ecosystem. Through this integration, GML emerges as a beacon guiding us toward a future where environmental stewardship is informed by data-driven insights, leading to more effective and sustainable solutions for the benefit of our planet and future generations.

The issues of human impact on the environment are evident and pose a threat to the health and well-being of future generations. Technogenic disturbances in coal mining sites, such as open pits, excavations, and industrial waste, pose risks to both human health and the environment. Open-pit coal mines not only frequently cause the destruction of natural ecosystems, including landscapes, vegetation, and biodiversity, but they also significantly contribute to greenhouse gas emissions into the atmosphere. Addressing the carbon footprint necessitates not only the use of renewable energy but also the restoration of disturbed landscapes and vegetation, including trees and shrubs. All of this is achieved by implementing biological remediation within technogenically disturbed territories. This process fosters a return of biological balance and establishes favorable conditions for plant and animal life, while at the same time reducing carbon footprint indicators. The biological remediation of areas affected by the mining activities of coal mines can create new economic opportunities. The reclaimed land can be utilized for various purposes such as agriculture, forestry, park development, and tourism, thereby contributing to local economic growth and job creation. When planning measures for land bioremediation, it is essential to analyze all quality indicators of the land. In this case, the selection of technologies such as plants, fertilizers, and microorganisms can effectively restore territories.