Ecological pest management (EPM) is gaining increased attention with concerns regarding human health and the environment. Planting green manure (GM) represents a significant practice in EPM; meanwhile, GM enhances crop production and reduces environmental footprints via its effect on the soil microbiome. GM's direct inhibitory effect on pests and its protective effect on natural enemies have been widely reported. However, the impact of GM's soil legacy effect on pests and the underlying molecular mechanisms remains poorly characterized. In this study, three-year field trials, greenhouse experiments, and multi-omics integration were conducted to address the gap. Compared to winter fallow treatment, GM significantly reduced the occurrence of rice major pests by 43.8–94.2 %, including Mythimna separata, Cnaphalocrocis medinalis, Chilo suppressalis, and rice planthoppers. The infestation rate of C. suppressalis, consumption by M. separata, and oviposition by Nilaparvata lugens were reduced by 64.3–87.4 %, 38.7–39.9 % and 45.3 %, respectively. Mechanistically, GM upregulated key defense-related genes and stimulated biosynthesis of flavonoids and alkaloids, alongside the accumulation of jasmonic acid and salicylic acid, indicating synergistic activation of induced systemic resistance in rice plants. Rhizosphere soil analysis revealed GM-driven enrichment of plant-beneficial taxa (Rhizophagus irregularis, Bradyrhizobium erythrophlei, Pseudolabrys sp.), alongside enhanced soil multifunctionality (N/C cycling) and nutrient mobilization. Our PLS-PM results supported a scenario in which GM-induced pest suppression is potentially mediated by microbiome-driven defense priming. Our findings provide fundamental insights into EPM and highlight how GM modulates the rhizosphere ecosystem and further enhances aboveground systemic resistance in rice. This study offers a potential solution for reducing synthetic inputs in crop production, which contributes to agroecosystem sustainability.
Environmental sciences, Environmental effects of industries and plants
Integration of AI into environmental regulation represents a significant advancement in data management. It offers promising results in both data protection plus algorithmic fairness. This research addresses the critical need for sustainable data protection in the era of ever evolving cyber threats. Traditional encryption methods face limitations in handling the dynamic nature of environmental data. This necessitates the exploration of advanced cryptographic techniques. The objective of this study is to evaluate how AI can enhance these techniques to ensure robust data protection while facilitating fair algorithmic management. The methodology involves a comprehensive review of current advancements in AI-enhanced homomorphic encryption (HE) and multi-party computation (MPC). It is coupled with an analysis of how these techniques can be applied to environmental data regulation. Key findings indicate that AI-driven dynamic key management, adaptive encryption schemes, and optimized computational efficiency in HE, alongside AI-enhanced protocol optimization and fault mitigation in MPC, significantly improve the security of environmental data processing. These findings highlight a crucial research gap in the intersection of AI, cyber laws, and environmental regulation, particularly in terms of addressing algorithmic bias, transparency, and accountability. The implications of this research underscore the need for stricter cyber laws. Also, the development of comprehensive regulations to safeguard sensitive environmental data. Future efforts should focus on refining AI systems to balance security with privacy and ensuring that regulatory frameworks can adapt to technological advancements. This study provides a foundation for future research aimed at achieving secure sustainable environmental data management through AI innovations.
As a distinct plant functional group, climbers critically sustain ecosystem structure and function globally. However, little is known about those in China. Here, we examine the diversity and distribution of Chinese climbers at a regional scale. First, climbing species data were collected. Then, Pearson correlations were conducted to assess relationships between environmental variables and climber species richness. Also, variation partitioning was used to reveal the pure and shared effects of four explanatory variable groups on species richness. A total of 3485 climber species (551 genera, 105 families) were recorded in China. Woody lianas dominated the climbing flora (64.73% of species) relative to herbaceous vines; twining represented the predominant mechanism (1829 species, 52.48%) relative to the others. Chinese climbers largely presented a pattern of species richness that decreased from south to north in China. Moreover, endemic and threatened climbers exhibited strong distributional congruence with all climbers. Additionally, four predictor groups (temperature, precipitation, geography, human impact) were found to jointly account for over 70% of species density variance across different climber types through variation partitioning, with precipitation’s pure effect dominating. Thus, Chinese climbers exhibit high diversity and an uneven distribution, primarily driven by precipitation. This study also provides a valuable reference on climbers at the regional scale for future studies.
Azad Ibn Ashraf, Eugene Mohareb, Maria Vahdati
et al.
The global generation of municipal solid waste (MSW) is expected to increase by 70 % by 2050, reaching 3.4 billion metric tons. Despite the need for proper waste management, less than 20 % of waste is recycled, and waste continues to end up in landfills. Waste management is a significant problem in Bangladesh and other rapidly urbanizing nations, exacerbated by densely populated housing coupled with inadequate infrastructure. The utilization of informal waste collectors arises from the government's frequent inability to offer sufficient waste collection and disposal services. A large number of Dhaka's informal sector workers depend on collecting waste for a living. In this study, the social life cycle assessment (S-LCA) is applied to analyze the social implications of formal and informal waste collectors on the waste management process in Uttara, Dhaka. Working conditions, human rights, health and safety, and socio-economic repercussions are the four primary areas of focus for the S-LCA. For the assessment, an indicator score ranging from 2 (best performance) to −2 (poor performance) was used. The data revealed that informal workers scored 0 for fair salaries, but formal workers received 1, showing that formal workers adhere to higher standards. Both groups obtained an average score of −2 in the social security subcategory, which is much lower than anticipated. Formal workers scored −2 on health and safety, while informal workers scored −1, indicating serious inadequacies in both categories. These findings highlight the need for stronger legislation and support systems to enhance waste collectors' working conditions in Dhaka and other similar cities throughout the world, as well as the considerable socioeconomic challenges they confront.
In the context of accelerating digital transformation and increasing environmental pressures, this study investigates how enterprise digitalization influences green dual innovation—comprising green exploratory innovation (EI) and green developmental innovation (DI)—within Chinese A-share listed firms from 2016 to 2023. Drawing upon fixed-effects and triple difference models, we empirically examine the mechanisms through which digital transformation (DCG) enhances green innovation and assess the mediating role of R&D investment and the moderating effect of government subsidies (GS). Results reveal that digital transformation significantly promotes both EI and DI, with a stronger effect on EI. R&D investment fully mediates this relationship, indicating that digital capabilities boost green innovation primarily through enhanced research capacity. Government subsidies exhibit a dual effect: they positively moderate the impact of digital transformation on EI, while dampening its influence on DI, possibly due to over-reliance on external funding. Heterogeneity analysis through triple difference methods uncovers substantial regional and industry-level variation—particularly among high-tech and heavily polluting firms in Eastern China—where digital transformation's green effects are constrained by strategic short-termism or technological limitations. These findings offer theoretical insights and practical implications for firms and policymakers aiming to align digital strategy with green transformation objectives under sustainability mandates.
Hidayatuz Zu'amah, Cicik Oktasari Handayani, Triyani Dewi
et al.
Excessive pesticide use and intensive shallot farming to satisfy high market demand can degrade soil quality and harm environmental sustainability. Sustainable agricultural practices, such as biochar and compost are alternatives to ensure long-term soil productivity and fertility. This study aimed to investigate the ability of biochar and compost made from bagasse waste to improve shallot growth, yield, nutrient absorption, and soil quality. The research used a factorial randomized block design in a greenhouse. The first factor was the application of biochar and compost (K1 = compost, K2 = biochar, K3 = biochar-compost (1:4/w:w) and K4 = biochar-compost (1:2/w:w), and the second factor was the dose of NPK fertilizer (0%, 50%, and 100% of the recommended dose). The planting medium used was an Inceptisol from shallot fields in Sukaharjo, Central Java. The variables measured included plant growth, yield components, soil chemical properties, and levels of N, P, and K in plants. The results showed that the combination of compost with 100% NPK fertilizer gave better results in plant growth, fresh and dry weight of plants and tubers, increased organic C, total N, and CEC of the soil, and increased the absorption of N, P, and K by plants.
Ana Ermiyati, Eko Rini Indrayatie, Uripto Trisno Santoso
et al.
Coal in South Kalimantan is widely used in industry and the Steam Electric Power Center (PLTU) as boiler fuel for power generation. Fly ash is a waste produced by about 80% of coal combustion. Fly ash solid waste, if not handled properly, can cause environmental pollution in water, air, and soil. The utilization of environmentally friendly fly ash is a crucial issue in reducing the environmental burden. One of them is by utilizing fly ash as an adsorbent. This study examines the characteristics and effectiveness of fly ash in South Kalimantan as an adsorbent for Fe, Mn, and Cd metal cations in acid mine drainage. Fly ash used is fly ash without activation (FA), activated fly ash with HCl 1 M (HFA), and activated with NaOH 1 M (NaFA).The three types of adsorbents (FA, HFA, and NaFA) have characteristics that include a dominant content of silica oxide (SiO2), alumina oxide (Al2O3), and iron oxide (Fe2O3), as well as spherical particles. NaFA has the largest surface area of 26.945 m2/g, FA 9.499 m2/g, and HFA 21.994 m2/g. NaFA has the highest absorption with optimum Fe of 99.9%, Mn of 94.8% and Cd of 99.9%. NaFA is more effective in reducing Fe metal ions compared to FA and HFA at an adsorbent mass of 10 g. Likewise with the metal ions Mn and Cd, NaFA is most effective in reducing both metal ions at an adsorbent mass of 30 g.
Abhijeet Maurya, Bhanu Pratap Singh and Ajay Kumar Sharma
Pollution is a major problem for urban cities and their associated industries. The pollution caused by industries is mainly because of the burning of fossil fuels. Some of the pollutants can be controlled by plantation, but the oxides of nitrogen cannot be controlled only by planting trees. Some extra efforts are required to minimize pollution associated with the normal functioning of the shop floor of the industries concerned but not affecting its performance. The fuel that is best for industrial use is the need of the hour. In this study, zinc oxide nanoparticles are used as an additive to the rice bran blended biodiesel and analyze the combustion, performance, and emission parameters in the single-cylinder four-stroke engine water-cooled powered by diesel normally utilized in industries at a constant speed and compression ratio. The available fuel alternatives for testing consist of multiple combinations of diesel fuel and RB biodiesel, each with varying proportions. Furthermore, many gasoline mixes additionally have Zinc Oxide nanoparticles at a concentration of 30 parts per million (ppm). The findings suggest that the brake-specific fuel consumption of Rice bran biodiesel combined with Zinc oxide nano additive exhibits a consistent enhancement, but the brake thermal efficiency declines in comparison to diesel fuel. The concentrations of hydrocarbon (HC) and oxides of nitrogen (NOX) have been reduced. However, there has been a small rise in carbon dioxide (CO2) and carbon monoxide (CO). When rice bran biodiesel fuel combined with Zinc Oxide nano additive was used, an abnormally high exhaust gas temperature (EGT) was detected. According to this research, the addition of Zinc Oxide nano additive to rice bran biodiesel blends improves performance and decreases the noxious exhaust emissions generated by diesel engines.
Environmental effects of industries and plants, Science (General)
The rapid population and economic growth in India have led to a significant rise in energy demand. Meeting this demand through renewable energy is critical, as fossil fuel-based energy generation contributes heavily to greenhouse gas emissions. Renewable energy development offers a sustainable and comprehensive approach to addressing the dual challenges of climate change mitigation and energy security. This paper evaluates the current status of renewable energy development in India and its role in mitigating the adverse effects of climate change. A detailed analysis of trends in solar, wind, and hydropower development reveals that India’s installed solar capacity is 82 GW, with an additional 88 GW under development. Wind energy accounts for approximately 46 GW of installed capacity, with 32 GW under development. Hydropower contributes 47 GW of installed capacity, with 18 GW under development. Together, these sources amount to approximately 175 GW of renewable energy capacity. Despite significant progress, India faces substantial challenges in achieving its ambitious renewable energy target of 500 GW by 2030. This paper highlights various programs initiated by the Government of India to promote renewable energy and ensure sustainable development. Additionally, it identifies key challenges in meeting the targets and recommends innovative solutions to overcome these hurdles, paving the way for a sustainable energy future.
Environmental effects of industries and plants, Science (General)
Anna Pražanová, Michael Fridrich, Jan Weinzettel
et al.
Recycling spent lithium-ion batteries (LIBs) is critical for enhancing environmental sustainability and resource conservation; however, the environmental and energy impacts of LIB recycling are not yet comprehensively understood due to the diverse applications of LIB cells and the variability in recycling technologies. This study presents a gate-to-gate life cycle assessment (LCA) of a recycling pre-treatment process at a small-scale plant in the Czech Republic, focusing on spent LIBs from electric vehicles (EVs) and consumer electronics cells (CECs). Using the SimaPro LCA software and the Ecoinvent 3.9 database, the analysis evaluated the environmental impact of recycling operations across several categories, including climate change, eutrophication, freshwater, and resource use, minerals and metals. The findings reveal that the recycling pre-treatment process for CECs achieves greater benefits in climate change mitigation compared to EV batteries, with a 5% lower impact for climate change associated with EV batteries relative to CECs. Moreover, the study highlights the effectiveness of optimized recycling practices in alleviating environmental burdens. A notable finding is the significance of secondary material recovery, particularly metals such as copper and aluminium, as these materials can substitute for primary raw materials, thereby minimizing resource use and reducing emissions. These aspects emphasize the need for high recovery efficiency to enhance environmental benefits. However, further research is essential to fully comprehend the environmental impacts of LIB recycling and to resolve uncertainties concerning battery composition and the effectiveness of different recycling technologies.
Marco A. Vargas, Luis A. Cisternas, Daniel Calisaya-Azpilcueta
The rapid expansion of mining operations has accelerated water resource depletion and increased energy consumption. To tackle these issues, the circular economy provides a framework for managing resource use and reducing environmental impacts. This study introduces an innovative method for assessing circularity in mineral processing, supported by a case study that demonstrates the methodology. The approach is based on circularity indices and employs Monte Carlo simulations to account for epistemic uncertainties that are typical in this field. If the results are not sufficiently robust, global sensitivity analysis is utilized to pinpoint critical variables that require refinement to enhance the outcomes. Additionally, multicriteria decision analysis methods are applied to further the study. In a case study focused on copper concentration through flotation, optimizing water recovery methods significantly improved water circularity metrics. Furthermore, dewatering technologies increased the system's circularity by 28 %, with similar improvements across different setups, reducing water extraction by approximately 42 % and lowering water loss from 0.56 in the base case to 0.29, a 48.6 % decrease. This highlights the effectiveness of dewatering technologies in circular resource management. Then, by applying multicriteria decision-making methods and giving more weight to general indicators related to the water–energy nexus, paste tailings thickening technology was identified as the best option for improving circularity and advancing sustainability. This methodology provides a solid foundation for assessing the transition from a recirculation-based economy to a truly circular model, offering global applicability for comparing technologies, companies, and other relevant factors in mineral processing.
Elena Beducci, Federica Acerbi, Anna De Carolis
et al.
In the manufacturing context, servitization emerges as a relevant path to improve sustainability. Notably, it can be applied to reconfigure industries' business models into circular ones, through multiple sustainable and circular strategies. Digital servitization, particularly, can enhance companies' sustainability along the products’ lifecycle, optimizing design and operations through collected data and supporting decision-making for end-of-life activities.Digitalization, servitization, and sustainability present promising synergies, nonetheless this topic requires more research. It is possible to observe a lack of understanding of the intersection of servitization, digitalization, and sustainability, and how they can be applied in the manufacturing context.This paper aims to investigate the connection between digital technologies, services, and sustainable strategies. The objective is to identify and characterize the linkages between digital technologies, services, and environmental and social sustainable strategies in the context of servitization. Through the implementation of a systematic literature review, the paper proposes an analysis of the state-of-the-art, identifying main trends and investigating specific relevant digital technologies, services, and sustainable impacts. The authors then propose a framework which clarifies how services, enabled by specific digital technologies, can be leveraged in the context of manufacturing product service systems to implement social and environmental sustainability strategies. Finally, the paper highlights opportunities for future research based on existing identified gaps.
Bacteriophage-bacteria interactions are central to microbial ecology, influencing evolution, biogeochemical cycles, and pathogen behavior. Most theoretical models assume static environments and passive bacterial hosts, neglecting the joint effects of bacterial traits and environmental fluctuations on coexistence dynamics. This limitation hinders the prediction of microbial persistence in dynamic ecosystems such as soils and oceans.Using a minimal ordinary differential equation framework, we show that the bacterial growth rate and the phage adsorption rate collectively determine three possible ecological outcomes: phage extinction, stable coexistence, or oscillation-induced extinction. Specifically, we demonstrate that environmental fluctuations can suppress destructive oscillations through resonance, promoting coexistence where static models otherwise predict collapse. Counterintuitively, we find that lower bacterial growth rates are helpful in enhancing survival under high infection pressure, elucidating the observed post-infection growth reduction.Our studies reframe bacterial hosts as active builders of ecological dynamics and environmental variation as a potential stabilizing force. Our findings thus bridge a key theory-experiment gap and provide a foundational framework for predicting microbial responses to environmental stress, which might have potential implications for phage therapy, microbiome management, and climate-impacted community resilience.
The application of spectral-shifting films in greenhouses to shift green light to red light has shown variable growth responses across crop species. However, the yield enhancement of crops under altered light quality is related to the collective effects of the specific biophysical characteristics of each species. Considering only one attribute of a crop has limitations in understanding the relationship between sunlight quality adjustments and crop growth performance. Therefore, this study aims to comprehensively link multiple plant phenotypic traits and daily light integral considering the physiological responses of crops to their growth outcomes under SF using artificial intelligence. Between 2021 and 2024, various leafy, fruiting, and root crops were grown in greenhouses covered with either PEF or SF, and leaf reflectance, leaf mass per area, chlorophyll content, daily light integral, and light saturation point were measured from the plants cultivated in each condition. 210 data points were collected, but there was insufficient data to train deep learning models, so a variational autoencoder was used for data augmentation. Most crop yields showed an average increase of 22.5% under SF. These data were used to train several models, including logistic regression, decision tree, random forest, XGBoost, and feedforward neural network (FFNN), aiming to binary classify whether there was a significant effect on yield with SF application. The FFNN achieved a high classification accuracy of 91.4% on a test dataset that was not used for training. This study provide insight into the complex interactions between leaf phenotypic and photosynthetic traits, environmental conditions, and solar spectral components by improving the ability to predict solar spectral shift effects using SF.
We estimate the impact of environmental law enforcement on violence in the Brazilian Amazon. The introduction of the Real-Time Deforestation Detection System (DETER), which enabled the government to monitor deforestation in real time and issue fines for illegal clearing, significantly reduced homicides in the region. To identify causal effects, we exploit exogenous variation in satellite monitoring generated by cloud cover as an instrument for enforcement intensity. Our estimates imply that the expansion of state presence through DETER prevented approximately 1,477 homicides per year, a 15\% reduction in homicides. These results show that a replicable environmental enforcement policy produces social benefits.
Abdelkader Laribi, Radjae Abdelouahad, Imane Tamine
et al.
Soil organic carbon (SOC) is an important component of soil and plays a crucial role in addressing climate change. As a key component of soil organic matter, SOC directly impacts soil fertility, water retention, nutrient cycling, and overall soil health. The determination of SOC concentrations in soil often relies on costly physical sampling and chemical analysis. The aim of this research was to build a predictive model of SOC using satellite imagery of Landsat 8 OLI/TIRS over an agricultural area (Oued El Alleug) in the north of Algeria. The statistical correlations between the spectral bands (B2 and B6) and chemically measured SOC concentrations showed that it is possible to predict spatially the SOC concentrations. The results also showed that the topographic variables are not determinant in the spatial prediction of SOC concentrations. The predicted model showed an acceptable performance with a coefficient of determination (R2) = 0.7 and a root mean square error (RMSE) = 7.08 g/kg during the validation phase. The results of this study are important, as they will facilitate decision-making in soil conservation practices and enhance land management, especially in areas facing increasing agricultural and environmental pressures.
P. Solanki, S. Jain, R. Mehrotra, P. Mago and S. Dagar
The rapid global plastic production of 348 million tonnes in 2018 has led to widespread environmental pollution, especially in terrestrial ecosystems. This study examines microplastics in agricultural soils, coming alarmingly. Particles ≤5 mm, which are defined as microplastics, have detrimental effects on the earth’s environment. Because of its ecological importance, soil acts as an important microplastic sink, affecting soil and plant health and microbial activity. A variety of factors contribute to microplastic pollution in agricultural soils, including plastic mulching, manure, agricultural products (silage nets, twine), sewage sludge, weathering, and other indirect processes. These microplastics migrate, threatening soil integrity and biodiversity. Soil microplastics are analyzed for size, volume fraction, and polymer. Common materials include polyethylene, polypropylene, polyamide, polystyrene, polyvinyl chloride, and polyesters. Techniques, including optical microscopy and spectroscopy, extract and analyze microplastics. This comprehensive review calls for increased concern about the ecological effects of microplastics in agricultural soils. It emphasizes the importance of managing plastics to solve environmental challenges. The integrated environmental assessment highlights the complex relationship between microplastics and soil ecosystems, providing insights into potential risks and suggesting strategies to combat this looming environmental threat.
Environmental effects of industries and plants, Science (General)
Feven Markos Hunde, Oliver Newton, Wojciech A. Hellwing
et al.
Using the high-resolution $N$-body cosmological simulation COLOR, we explore the cosmic web (CW) environmental effects on subhalo populations and their internal properties. We use CaCTus, which incorporates an implementation of the state-of-the-art segmentation method NEXUS+, to delineate the simulation volume into nodes, filaments, walls, and voids. We group host haloes by virial mass and segment each mass bin into consecutive CW elements. This reveals that subhalo populations in hosts within specific environments differ on average from the cosmic mean. The subhalo mass function is affected strongly, where hosts in filaments typically contain more subhaloes ($5$ to $20\%$), while hosts in voids are subhalo-poor, with $25\%$ fewer subhaloes. We find that the abundance of the most massive subhaloes, with reduced masses of $μ\equiv M_\mathrm{sub}/M_{200}$ is most sensitive to the CW environment. A corresponding picture emerges when looking at subhalo mass fractions, $f_\mathrm{sub}$, where the filament hosts are significantly more `granular' (having higher $f_\mathrm{sub}$) than the cosmic mean, while the void hosts have much smoother density distributions (with $f_\mathrm{sub}$ lower by $2$ - $20\%$ than the mean). Finally, when we look at the subhalo internal kinematic $V_{\rm max}$-$R_{\rm max}$ relations, we find that subhaloes located in the void and wall hosts exhibit density profiles with lower concentrations than the mean, while the filament hosts demonstrate much more concentrated mass profiles. Across all our samples, the effect of the CW environment generally strengthens with decreasing host halo virial mass. Our results show that host location in the large-scale CW introduces significant systematic effects on internal subhalo properties and population statistics.
Christophe Nicolet, Antoine Béguin, Matthieu Dreyer
et al.
This paper is addressing the quantification and the comparison of pumped storage power plants, PSPP, contribution to synchronous inertia and synthetic inertia when fixed speed and variable speed motor-generators technologies are considered, respectively. Therefore, a grid stability study was conducted by means of 1D SIMSEN simulation for the 2 x 395 MW PSPP Frades 2 in Portugal with both fixed speed and variable speed technologies in case of operation connected to an infinite power network or to an islanded 4.4 GW synchronous power network.
Pablo Hernández-Cámara, Jorge Vila-Tomás, Paula Dauden-Oliver
et al.
Autonomous driving is a challenging scenario for image segmentation due to the presence of uncontrolled environmental conditions and the eventually catastrophic consequences of failures. Previous work suggested that a biologically motivated computation, the so-called Divisive Normalization, could be useful to deal with image variability, but its effects have not been systematically studied over different data sources and environmental factors. Here we put segmentation U-nets augmented with Divisive Normalization to work far from training conditions to find where this adaptation is more critical. We categorize the scenes according to their radiance level and dynamic range (day/night), and according to their achromatic/chromatic contrasts. We also consider video game (synthetic) images to broaden the range of environments. We check the performance in the extreme percentiles of such categorization. Then, we push the limits further by artificially modifying the images in perceptually/environmentally relevant dimensions: luminance, contrasts and spectral radiance. Results show that neural networks with Divisive Normalization get better results in all the scenarios and their performance remains more stable with regard to the considered environmental factors and nature of the source. Finally, we explain the improvements in segmentation performance in two ways: (1) by quantifying the invariance of the responses that incorporate Divisive Normalization, and (2) by illustrating the adaptive nonlinearity of the different layers that depends on the local activity.