Vedelya Istighfara, Dermiyati Dermiyati, Rinawati Rinawati
et al.
Heavy-metal contamination in river systems poses an escalating threat to aquatic ecosystems and public health. This study provides the first integrated geospatial assessment of zinc (Zn), copper (Cu), cadmium (Cd), iron (Fe), and manganese (Mn) in water and sediment of the Way Ratai River, an area increasingly impacted by artisanal gold processing. Inductively coupled plasma optical emission spectroscopy (ICP-OES) and X-ray fluorescence were used to evaluate water and sediment samples that were gathered at five key locations. Spatial interpolation via ArcGIS with an inverse distance weighting algorithm identified contamination hotspots. In river water, Zn averaged 0.05 ppm, Mn 0.009 ppm, and Fe 0.075 ppm, while Cd and Cu were below detection limits. Sediment concentrations were markedly elevated: Zn 702.62 ppm, Mn 685.60 ppm, Fe 2,954.72 ppm, and Cu 253.84 ppm. All detected metals exceeded regional background thresholds, particularly near gold-processing effluent zones, indicating significant geochemical accumulation. These findings underscore the urgent need for stringent waste-management protocols—such as coagulation and adsorption systems—to mitigate heavy-metal release and protect downstream ecosystems and human communities. This research closes a critical data gap and offers a spatially resolved framework for monitoring and remediation strategies in mining-impacted watersheds.
Ali Marefat, Abooali Golzary, Fumitake Takahashi
et al.
Effective management of organic waste plays a vital role in addressing environmental challenges and advancing sustainable development goals. This review article provides a comprehensive financial assessment of anaerobic digestion (AD) systems for treating the organic fraction of municipal solid waste (OFMSW). Previous studies on AD systems have largely been confined to local case studies with findings that cannot be generalized beyond their specific regions, and they have often overlooked the combined influence of technical, economic, and political factors on financial performance. The study begins by identifying the gaps and limitations in existing financial models for AD systems. It then develops an enhanced financial framework and uses scenario-based analyses to assess the financial feasibility of AD technologies. The model outputs indicate that the financial feasibility of AD systems is heavily influenced by national energy policies and environmental regulations. It is revealed that insufficient government support for energy tariffs—particularly in countries experiencing economic instability—serves as a major barrier to the cost-effectiveness of AD systems. In contrast, in economically stable regions, the financial sustainability of these systems is primarily challenged by stringent environmental regulations specifically related to digestate disposal. To overcome these challenges and further strengthen the financial performance of AD systems, future research should prioritize AI-driven optimization to enhance operational efficiency, reduce costs, increase energy output, and ultimately improve financial sustainability. By addressing existing barriers and proposing actionable recommendations, this review aims to foster the adoption of AD technologies as a cornerstone of sustainable waste management practices.
Alexandria Tanciongco, Jessie Samaniego, Cris Reven Gibaga
et al.
With the rising demand for copper driven by technological advancements and the depletion of high-grade resources, there is increasing interest in recycling secondary sources to extract copper and reduce mining tailings. This study examined legacy copper mine tailings from Benguet, Philippines, to assess their reprocessing potential, using historical data and geochemical analysis. The tailings have a loamy sand texture, with measured porosity of 39.2% and a permeability coefficient of 1.0952 x 10-² cm s-1, indicating low porosity and medium permeability. Water infiltration rates vary from 0.37 to 4.71 cm min-1, suggesting a heterogeneous particle size distribution. The primary components of the tailings are quartz, biotite, and plagioclase feldspars, with minor amounts of calcite, magnetite, chalcopyrite, and pyrite. Copper (0.19%) and sulfur (0.11%) ratios indicate copper is present in both chalcopyrite and smectite minerals. Notably, the highest copper concentration is found in particles smaller than 63µm, with 0.29 wt.% Cu and 0.24 g t-1 Au, or 0.434 wt.% Cu equivalent, exceeding the mining company's cut-off grade of 0.274% CuEq. This study highlights a promising opportunity to recycle secondary sources of copper and gold, helping to meet the increasing demand for these metals driven by modern technological advancements.
Land cover changes resulting from mining activities in Central Bangka District have often led to environmental degradation, significant challenges for local communities, and disruptions to spatial utilization. This study aims to identify land cover change patterns within the tin mining business license (IUP) area from 2014 to 2022 and evaluate their impacts on ecosystems and land use. The study employed the Maximum Likelihood Classification (MLC) method for satellite image analysis to detect land cover changes. The results indicated that mining land expanded by 2,117.29 ha between 2018 and 2022, primarily due to the conversion of secondary and natural vegetation. Meanwhile, secondary vegetation declined significantly, with 4,187.46 ha reduction from 2014 to 2022, highlighting the extensive exploitation of land for mining activities. Additionally, an increase in water bodies was observed due to the formation of water-filled mine voids, locally known as "kolong". The classification accuracy assessment demonstrated high reliability, with Kappa coefficients of 93.7% in 2014, 92.73% in 2018, and 94.5% in 2022, confirming the effectiveness of the MLC method in detecting land cover changes. The findings of this study provide critical insights for post-mining land management, emphasizing the need for enhanced reclamation and revegetation strategies. A more comprehensive understanding of land change dynamics is expected to support sustainable spatial planning and inform environmental impact mitigation policies in Central Bangka District.
Valeria Mezzanotte, Sara Venturelli, Riccardo Paoli
et al.
Industrial laundries need large amounts of energy and water and, thus, generate large amounts of wastewater, due to the core washing, drying and ironing processes and to the transport of linen and chemicals. The presented Life-Cycle Assessment (LCA) concerns an Italian industrial laundry, and is based on primary data collected from the facility, complemented by information from literature, supporting databases (Ecoinvent 3.8), and technical datasheets. The analysis covers the entire cycle of linen processing (material extraction and manufacturing, transport, logistics, laundry processes, wastewater treatment and reuse, packaging, and solid waste management). The defined Functional Unit (FU) is 1 kg of linen. The LCA, carried out by SimaPro 9.2 and ReCiPe 2016 H, indicates a total impact of 12.77 mPt/FU, chiefly deriving from washing (4.62 mPt), ironing (4.29 mPt), and drying (1.56 mPt). Detergents and washing agents contribute significantly to the impact of the washing phase. 'Fine particulate formation' is the most affected impact category (5.18 mPt). The initial results suggested that generating renewable energy on-site could reduce the environmental impact by 19.7%. Solar photovoltaic panels were installed in 2023, and the actual energy production exceeded expectations, indicating an even greater reduction in the laundry environmental footprint.
Existing buildings contribute nearly 40 % of Europe's overall energy usage and represent about 36 % of the related greenhouse gas emissions from energy. Thus, the governments set retrofit targets for residential and non-residential buildings. The European Union has recently projected to transition from net-zero energy buildings to zero-carbon buildings by 2030, expanding the current building environmental performance standards to include embodied and operational carbon emissions. The study highlights the embodied carbon (EC) emissions linked to materials used in the deep retrofit measures for homes in Ireland. The research developed a novel methodology for evaluating the EC in deep retrofitted homes with defined benchmarks based on the results of the case studies. The findings show that the upfront and the whole-life EC averages of the deep retrofitted homes in Ireland are 347 and 662 kgCO2e/m2, respectively. These averages represent a benchmark for the current market practice and a starting point for developing EC reduction targets for upcoming projects. The study developed a methodology for reducing the EC of the retrofitted homes during the design stage through a group of material substitution scenarios for the most impactful materials in the case studies. Eleven materials were replaced with low-carbon alternatives available in the Irish market, resulting in an average of 23 % and 20 % reductions in the upfront and the whole-life EC emissions, respectively. Ultimately, the study is a guide for the retrofitting specialists to evaluate and compare the EC of their designs with the developed benchmarks, with solutions to achieve the reduction targets.
Nur Robifahmi, Muftia Hanani, Taufiq Bachtiar
et al.
The degraded Latosol soil is characterized by its acidity and low organic matter content, which limits rice productivity. Improving the chemical properties of the soil is crucial for supporting the sustainability of paddy fields. This study aimed to enhance these properties using silica-enriched biochar through gamma irradiation as a biofertilizer carrier. Rice husk biochar was selected for its resistance to decomposition and was enriched with SiO? to fulfill the nutritional needs of rice plants. The treatments tested were combinations of biochar-silica formulas (F0 = no formula, F1 = biochar-silica with rice husk ash, and F2 = biochar-silica with zeolite) and NPK fertilizer doses (R0 = no fertilizer, R1 = 50% recommended dose, R2 = 100% recommended dose). The results showed that the F1 formula combined with the full NPK dose (R2) significantly improved soil structure, increased organic carbon content, and enhanced nutrient uptake efficiency, which in turn, promoted higher rice productivity. The use of silica-enriched biochar as a carrier for biofertilizers has proven effective in supporting the sustainability of paddy soils. The F1 formula with a full dose of NPK can be recommended to enhance rice productivity while maintaining soil fertility.
Multivariate time series (MTS) are ubiquitous in domains such as healthcare, climate science, and industrial monitoring, but their high dimensionality, limited labeled data, and non-stationary nature pose significant challenges for conventional machine learning methods. While recent self-supervised learning (SSL) approaches mitigate label scarcity by data augmentations or time point-based contrastive strategy, they neglect the intrinsic periodic structure of MTS and fail to capture the dynamic evolution of latent states. We propose PLanTS, a periodicity-aware self-supervised learning framework that explicitly models irregular latent states and their transitions. We first designed a period-aware multi-granularity patching mechanism and a generalized contrastive loss to preserve both instance-level and state-level similarities across multiple temporal resolutions. To further capture temporal dynamics, we design a next-transition prediction pretext task that encourages representations to encode predictive information about future state evolution. We evaluate PLanTS across a wide range of downstream tasks-including multi-class and multi-label classification, forecasting, trajectory tracking and anomaly detection. PLanTS consistently improves the representation quality over existing SSL methods and demonstrates superior runtime efficiency compared to DTW-based methods.
Sonia Bujok, Tomasz Pańczyk, Kosma Szutkowski
et al.
To clean or not to clean? The solution to this dilemma is related to understanding the plasticiser migration which has a few practical implications for the state of museum artefacts made of plasticised poly(vinyl chloride) - PVC and objects stored in their vicinity. The consequences of this process encompass aesthetic changes due to the presence of exudates and dust deposition, an increase in air pollution and the development of mechanical stresses. Therefore, this paper discusses the plasticiser migration in PVC to provide evidence and support the development of recommendations and guidelines for conservators, collection managers and heritage scientists. Particularly, the investigation is focused on the migration of the ortho-phthalates representing the group of the most abundant plasticisers in PVC collections. The predominance of inner diffusion or surface emission (evaporation) determining the rate-limiting step of the overall migration process is considered a fundament for understanding the potential environmental and mechanical risk. According to this concept, general correlations for various ortho-phthalates are proposed depending on their molar mass with the support of molecular dynamics simulations and NMR diffusometry. The study reveals that for the majority of the PVC objects in collections, the risk of accelerated migration upon mild removal of surface plasticiser exudate is low. Thus, surface cleaning would allow for diminishing dust deposition and air pollution by phthalate-emitting objects in a museum environment. Bearing in mind simplicity and the need for fast decision-supporting solutions, the step-by-step protocol for non-destructive identification and quantification of plasticisers in objects made of or containing plasticised PVC, determination of the physical state of investigated artefacts and rate-limiting process of plasticiser migration is proposed.
Designing for sufficiency is one of many approaches that could foster more moderate and sustainable digital practices. Based on the Sustainable Information and Communication Technologies (ICT) and Human-Computer Interaction (HCI) literature, we identify five environmental settings categories. However, our analysis of three mobile OS and nine representative applications shows an overall lack of environmental concerns in settings design, leading us to identify six pervasive anti-patterns. Environmental settings, where they exist, are set on the most intensive option by default. They are not presented as such, are not easily accessible, and offer little explanation of their impact. Instead, they encourage more intensive use. Based on these findings, we create a design workbook that explores design principles for environmental settings: presenting the environmental potential of settings; shifting to environmentally neutral states; previewing effects to encourage moderate use; rethinking defaults; facilitating settings access and; exploring more frugal settings. Building upon this workbook, we discuss how settings can tie individual behaviors to systemic factors.
E. E. Salman, A. M. Akol, J. S. Abdel Hamza and Ahmed Samir Naje
In this study, the AquaCrop model was employed to analyze the impact of projected future climate changes on the water usage and biomass production of potato crops in Babylon, Iraq, under varying irrigation methods. The irrigation techniques evaluated included sprinkler irrigation, surface drip irrigation, and subsurface drip irrigation at depths of 10 cm and 20 cm. The study involved simulating and forecasting conditions for the year 2050, comparing them to current conditions. The model measured and predicted the evapotranspiration (ETa) and actual biomass of potato crops for 2050 using the RCP 8.5 scenarios, which outline different trajectories for greenhouse gas emissions. The AquaCrop model was calibrated and validated using statistical measures such as the R2, RMSE, CV, EF, and D, achieving a 99% accuracy level in its performance. The findings suggest that using drip irrigation systems and applying the AquaCrop model significantly mitigates the adverse effects of environmental stress on desert soils and enhances sustainable agricultural practices in arid regions.
Environmental effects of industries and plants, Science (General)
Yamileth C. Herrera, Ronald R. Gutierrez, Carlos Pacheco-Bustos
In small and medium-sized cities from developing countries, the early selection of integrated wastewater management systems is challenging due to the lack or limitations in the availability of basic information and skilled professionals. This study presents CPESI, a cumulative processes-based parsimonious workflow for early evaluation of sanitary infrastructure. CPESI is aimed to provide a simple, objective, and systematic analysis framework at the early stages of development of sanitary systems in underdeveloped nations. CPESI was applied to evaluate sanitation system alternatives for Riohacha (Colombia) in three stages, namely, (1) an initial assessment of citizens acceptability of the alternatives and analysis of basic laboratory testing; (2) a process analysis and technical-economic evaluation of alternatives based on CAPEX and OPEX indicators; and (3) engineering judgment to select the most viable alternative through multi-criteria evaluation. Our results suggest that CPESI could be highly replicable in developing countries and that it has the potential to expedite the alternatives assessment process when compared to data-intensive methods and expert requirements. Several researchers have highlighted the need to develop tools suitable to evaluate SDG 6 in developing nations. We believe that CPESI has the potential to contribute to that end.
Fabrice Mayran de Chamisso, Loïc Cotten, Valentine Dhers
et al.
With the advent of multispectral imagery and AI, there have been numerous works on automatic plant segmentation for purposes such as counting, picking, health monitoring, localized pesticide delivery, etc. In this paper, we tackle the related problem of automatic and selective plant-clearing in a sustainable forestry context, where an autonomous machine has to detect and avoid specific plants while clearing any weeds which may compete with the species being cultivated. Such an autonomous system requires a high level of robustness to weather conditions, plant variability, terrain and weeds while remaining cheap and easy to maintain. We notably discuss the lack of robustness of spectral imagery, investigate the impact of the reference database's size and discuss issues specific to AI systems operating in uncontrolled environments.
This paper examines the labor market impacts of the 2015 Mariana Dam disaster in Brazil. It contrasts two theoretical models: an urban spatial equilibrium model and a factor of production model, with diverging perspectives on environmental influences on labor outcomes. Utilizing rich national administrative and spatial data, the study reveals that the unusual environmental alteration, with minimal human capital loss, primarily affected outcomes via the factor of production channel. Nevertheless, spatial equilibrium dynamics are discernible within certain market segments. This research contributes to the growing literature on environmental changes and its economic consequences.
This paper is an invitation to the process systems engineering community to change the paradigm for process plants. The goal is to achieve much easier convergence while retaining accuracy on par with the rigorous models. Accurate plant models of existing plants can be linear or much less nonlinear if they are based on mass component flows and stream properties per unit mass properties instead of molar flows and mole fractions. Accurate stream properties per unit mass can be calculated at stream specific conditions by linear approximations which in many instances eliminates mole fraction-based flash calculations. Hybrid data-driven node models fit naturally in this paradigm, since they used measured data, which is either in mass or in volumetric units, but never in moles. Instantiation of models at all levels of abstraction (planning, scheduling, optimization, and control models) from the same plant topology representation will ensure inheritance of solutions from mass-only to mass-and-energy to mass-and-energy-and-stream-properties, thereby ensuring consistency of solutions between these models. None of the existing software provides inheritance between different levels of plant abstraction (i.e. inheritance between models for different business applications) or different levels of abstractions per plant sections or per time periods, which motivates this exposition.
G. Sasi Kumar, G. Nagaraju, D. Rohith and A. Vasudevarao
With India’s population growing at a rapid pace, traditional agriculture will have a tough time meeting future food demands. Water availability and conservation are major concerns for farmers. This paper aims to discuss the aspects related to designing and fabricating an automatic irrigation system using the Internet of Things (IoT) which will save the farmer’s time and money significantly. Human intervention in fields will be reduced. Changes in soil moisture are detected by soil moisture sensors and irrigation is automated using IoT. The proposed system is most economical for underdeveloped places because it is very cost-effective. Based on the soil moisture content, the sensor detects and sends signals to the node MCU, which activates the motor. When the plants receive enough water, the motor automatically shuts off. The user will be alerted about the soil’s moisture content through his mobile phone. The proposed smart irrigation system is implemented at our campus which conserves energy and water.
Environmental effects of industries and plants, Science (General)
Dustin Wright, Christian Igel, Gabrielle Samuel
et al.
Artificial intelligence (AI) is currently spearheaded by machine learning (ML) methods such as deep learning which have accelerated progress on many tasks thought to be out of reach of AI. These recent ML methods are often compute hungry, energy intensive, and result in significant green house gas emissions, a known driver of anthropogenic climate change. Additionally, the platforms on which ML systems run are associated with environmental impacts that go beyond the energy consumption driven carbon emissions. The primary solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the compute and energy efficiency with which ML systems operate. In this perspective, we argue that it is time to look beyond efficiency in order to make ML more environmentally sustainable. We present three high-level discrepancies between the many variables that influence the efficiency of ML and the environmental sustainability of ML. Firstly, we discuss how compute efficiency does not imply energy efficiency or carbon efficiency. Second, we present the unexpected effects of efficiency on operational emissions throughout the ML model life cycle. And, finally, we explore the broader environmental impacts that are not accounted by efficiency. These discrepancies show as to why efficiency alone is not enough to remedy the adverse environmental impacts of ML. Instead, we argue for systems thinking as the next step towards holistically improving the environmental sustainability of ML.
There are two main life cycles in plants, annual and perennial. These life cycles are associated with different traits that determine ecosystem function. Although life cycles are textbook examples of plant adaptation to different environments, we lack comprehensive knowledge regarding their global distributional patterns. Here, we assembled an extensive database of plant life cycle assignments of 235,000 plant species coupled with millions of georeferenced data points to map the worldwide biogeography thereof. We found that annuals are half as common as initially thought, accounting for only 6% of plant species. Our analyses indicate annuals are favored in hot and dry regions. However, a more accurate model shows annual species' prevalence is driven by temperature and precipitation in the driest quarter (rather than yearly means), explaining, for example, why some Mediterranean systems have more annuals than deserts. Furthermore, this pattern remains consistent among different families, indicating convergent evolution. Finally, we demonstrate that increasing climate variability and anthropogenic disturbance increase annual favorability. Considering future climate change, we predict an increase in annual prevalence for 69% of the world's ecoregions by 2060. Overall, our analyses raise concerns for ecosystem services provided by perennials, as ongoing changes are leading to a more annuals-dominated world.