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.

This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (χ²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, χ², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns.

This paper examines electronic waste and cycling in Lebanon. It describes the current situation regarding e-waste among government agencies and non-governmental organizations. It addresses two research questions: The first one asks if the Lebanese society and government are aware of the dangers posed by electronic waste and whether any action has been taken to prevent an environmental catastrophe. The second question asks about Lebanese attitudes toward e-waste and whether they are willing to fight against it. Interviews provided the first question’s responses. The authors have visited Organization A and NGO B. The first is worried about gathering waste in more prominent Beirut, while the second targets spreading attention to e-waste’s risks on legislative and social levels the same. Question two was discussed through surveys filled out by arbitrary people from Lebanese society. The answers to both research questions came in a manner that demonstrates the two hypotheses expected toward the start of the study, specifically that e-waste represents an incredible danger to the Lebanese climate. Hypothesis two, if climate neighborliness and proclivity to right e-garbage removal rely upon the instructive level of some random resident, has been confirmed while analyzing the answers in the survey.

Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.

International audience | Pas un mois ne passe sans que la pollution plastique ne fasse l’actualité d’une façon ou d’une autre. Il faut dire que nul écosystème n’échappe à la pollution plastique. En moins d’un siècle, nous avons produit deux fois plus de plastique en masse qu’il n’existe d’animaux sur terre.La majeure partie de ces plastiques deviennent finalement des déchets et contribuent à la saturation d’un système de gestion déjà à bout de souffle. Mais la problématique de la pollution plastique va bien au-delà de la question des déchets mal gérés : les plastiques polluent à toutes les étapes de leur cycle de vie. C’est ainsi qu’ils affectent tous les écosystèmes, du fond des océans jusqu’à l’air que nous respirons, en passant par les sols qui nous nourrissent.Les scientifiques ne cessent d’alerter sur cette pollution qui a conduit à la plastification de notre planète en nuisant profondément à la santé humaine. Ces derniers ont un rôle à jouer dans les négociations internationales, comme à Ottawa (Canada) en avril 2024. Il s’agissait de la quatrième et avant-dernière session de négociation du traité mondial contre la pollution plastique (CIN-4). Depuis, le travail continue jusqu’à la dernière étape pour la finalisation du Traité, en novembre 2024 à Busan, en Corée du Sud.

There is growing evidence of negative impacts of antidepressants on behavior of aquatic non-target organisms. Accurate environmental risk assessment requires an understanding of whether antidepressants with similar modes of action have consistent negative impacts. Here, we tested the effect of acute exposure to two antidepressants, fluoxetine and venlafaxine (0–50 µg/L), on the behavior of non-target organism, i.e., freshwater pond snail, Lymnaea stagnalis. As compounds interact with chemical cues in the aquatic ecosystems, we also tested whether the effects altered in the presence of bile extract containing 5α-cyprinol sulfate (5α-CPS), a characterized kairomone of a natural predator, common carp (Cyprinus carpio). Behavior was studied using automated tracking and analysis of various locomotion parameters of L. stagnalis. Our results suggest that there are differences in the effects on locomotion upon exposure to venlafaxine and fluoxetine. We found strong evidence for a non-monotonic dose response on venlafaxine exposure, whereas fluoxetine only showed weak evidence of altered locomotion for a specific concentration. Combined exposure to compounds and 5α-CPS reduced the intensity of effects observed in the absence of 5α-CPS, possibly due to reduced bioavailability of the compounds. The results highlight the need for acknowledging different mechanisms of action among antidepressants while investigating their environmental risks. In addition, our results underline the importance of reporting non-significant effects and acknowledging individual variation in behavior for environmental risk assessment.

This study was conducted in Hezhang County, Bijie City, Guizhou Province. The soil in the zinc smelting area has been contaminated with cadmium, lead, and zinc. Therefore, these elements are the focus of this research. Rice husk biochar was used as the passivation material. The Fourier infrared spectrum was utilized to study the biochar’s morphology, element content, mineral composition, structure, and surface functional groups. Moreover, the physical and chemical properties of the biochar were analyzed to explore its passivation effect. Biochar is beneficial in the cleaning of cadmium, lead, and zinc minerals and can be used for the passivation of heavy metals in contaminated soil. This study aims to understand the detailed mechanism behind this process and provide experimental data and ideas for pollution control. The results indicate that the biochar contains many functional groups, including -OH, C-H, C-O, C=O, C=C, and C-O-C. It also consists of a significant quantity of potassium salt, calcite, and quartz. Biochar has a noticeable pore structure, and as the pyrolysis temperature increases, the pore structure becomes more developed and thinner, with a smooth surface. The main minerals in the soil are quartz, mica, zeolite, illite, and chlorite. The aromatic degree of biochar increased with pyrolysis temperature. In contrast, the aromatic degree and polarity first increased and then decreased. The 0.2-0.45 mm biochar exhibited the best passivation effect on cadmium, lead, and zinc.

This study was conducted in Barangay Badiangan, Ajuy, Iloilo City, Philippines (11°10’N, 122°58’E) to determine the effects of rainfall intensity and other rainfall-derived parameters on the directional components of splash erosion in hillslopes. There are five experimental set-ups with slope angles ranging from 0% to 48% were tested under natural rainfall conditions using a modified splash collector. The data collected shows that kinetic energy, slope, and rainfall intensity have shown significant effects on splash erosion. The models obtained using regression analysis are 𝑄𝑄𝑑𝑑𝑑𝑑𝑑𝑑=0.0093(𝐾𝐾𝐾𝐾0.80) and 𝑄𝑄𝑡 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡=0.060(𝐾𝐾𝐾𝐾0.107)(𝑆𝑆0.700)(𝐼𝐼200.700) . The model equation performance has been validated using the Standard Error of Estimates with values of 12 and 9.4 for splash detachment and splash transport, respectively. The constants used for kinetic energy in detachment and slope in transport align with the research by Quansah (1981) for sandy soil, which is similar (the characteristics) to the soil at our research site. Additionally, rainfall intensity, especially with a 20-min duration, generated the best model as it yielded the lowest SEE value for all cases.