Plant Location Research Articles

Abstract Purpose Europe aims to decarbonize its economy by 2050, which implies a significant deployment of renewables and energy storage technologies. Offshore low-head pumped hydro storage (O-PHS) is presented as an alternative solution for coastal countries with shallow seas and flat topography as a technology for grid-scale energy storage. Methods We conduct a Life Cycle Assessment (LCA) for the construction, operation, and maintenance stages of an O-PHS plant located in the North Sea, with a rated installed power of 2 GW and an average daily storage capacity of 8 GWh. We further compare O-PHS with conventional pumped hydro storage (C-PHS) in two inland European locations and lithium iron phosphate (LFP) batteries. Due to the location of the O-PHS plant, offshore wind electricity generation is assumed. Although the study focuses on climate change, the results for all 16 environmental impact categories of the European Product Environmental Footprint methodology are provided. Results and discussion We find that the O-PHS plant’s construction, maintenance, and operation emits around 33 gCO2eq/kWh. When comparing technologies, O-PHS greenhouse gas (GHG) emissions are slightly higher than C-PHS in the Alpine region and LFP batteries. In contrast, C-PHS results in the non-Alpine region are twice as high as the rest of the technology values. From these emissions, we see that the impacts related to electricity storage are roughly the same as those related to electricity generation. In other words, the use of O-PHS technology doubles the emissions from offshore wind farms. Although this may seem a high premium to pay, it becomes a relatively low value when comparing it to the GHG emissions from the electricity mix from surrounding countries like Germany or the Netherlands. On the other hand, the high demand for steel, copper, and magnets, together with efficiency losses, makes turbines a hotspot for the O-PHS plant in all environmental indicators. Conclusion This article urges engineers working in the O-PHS technology to focus on the turbines, increasing efficiency and considering circularity strategies during the design phase, including lifetime extension and recycling to reduce emissions across all impact categories.

This paper develops novel robust de novo programming models to accurately select optimal locations for concentrated solar power (CSP) plants under high levels of uncertainty. The CSP selection problem is a typical uncertain multi-objective decision-making (MODM) optimal design challenge that involves conflicting environmental, societal, and economic criteria. This paper makes the following contributions: (i) it develops robust counterparts to the conventional de novo programming (DNP) model and its meta-goal programming solution procedure to address a wide range of decision-making problems under conditions of uncertainty, (ii) it proposes a robust revised multi-choice DNP model, and (iii) it contributes to ongoing debates on sustainable development and clean energy transitions by identifying optimal locations for CSP plants in Morocco. The proposed robust methodologies provide decision-makers with increased flexibility for addressing uncertainty in MODM problems, allowing them to express their level of conservatism and preferences by setting priority weights, defining aspiration levels, and merging original explicit goals into meta-goals. Finally, the hypothetical application illustrates the effectiveness of the proposed formulations and demonstrates that they can assist decision-makers in identifying the optimal locations for CSP plants in Morocco under high levels of uncertainty.

Butterflies utilize visual, odour and tactile cues, individually or in combination, to navigate their surroundings and make decisions. The effectiveness of these cues varies based on distance and context. Colour is a crucial visual cue across multiple behavioural contexts, including when searching for oviposition sites. We investigated the multimodal integration of information and its modulation of colour preferences in the Common Emigrant butterfly, Catopsilia pomona. Specifically, we tested their preference towards green colour in different scenarios including during the phase they are expected to prioritize finding host plants for oviposition. We offered virgin and mated females and males a choice of four colours including green. We show that mated females prefer green but only in the presence of the odour of their preferred host plant. Virgin females and males, irrespective of mating status, preferred colours other than green. Our results suggest that host plant odour and colour are both salient cues for butterflies, and butterflies use them synergistically to find leaves to oviposit. The sex-specific preference towards green, and that green is preferred only under some contexts, highlights the adaptive plasticity of colour preferences in butterflies.

This study aims to measure the heavy metal concentration in soil and frequently used medicinal plants collected from various environmental locations, while evaluating the influence of these sites on the mineral composition of the plants. Plant and soil samples of Calotropis procera, Euphorbia hirta, Achyranthes aspera, Cynodon dactylon and Argemone mexicana were collected from four different environmental locations of the northern region of India namely: Thermal power plant (TPP), Industrial area (IA), Brick kiln (BK) and Heavy traffic area (HTA). Essential metals i.e. Mn, Fe, Co, Zn and potentially toxic metals i.e. Cr, Ni, Cd, Hg and Pb were quantified in soil and plant samples using Atomic absorption spectrophotometry (AAS) and subsequently compared. The highest concentration of iron (Fe) (70.8±0.02 ppm) was found in soil from the thermal power plant area. The most toxic heavy metal lead (Pb) (8.3± 0.01ppm) and cadmium (Cd) (0.1±0.001ppm) were observed highest from Heavy traffic area sites in Calotropis procera and Euphorbia hirta respectively, although found in permissible limit of 10 and 0.3 ppm respectively set by WHO (World Health Organisation) for herbal products. The concentration of Hg remained below detection limit in all tested samples. There is a significant variation in plant location and heavy metal concentration (P≤0.05). Heavy metals were found below permissible limits in all tested samples. However, continuous consumption of some toxic metals can lead to accumulation in the body. Therefore, in order to prevent health risks, it is recommended that cultivation of medicinal plants should be prohibited near environmentally polluted site especially heavy traffic area.

Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems.

The study optimizes the location and size of wind power plants (WPs), Thyristor Controlled Series Capacitor (TCSC), and Static VAr Compensator (SVC) in the IEEE 30-bus transmission power grid to minimize the total cost of loss, fuel of thermal generating units, and TCSC and SVC in one operating hour. The investment, operation, and maintenance costs of the WPs, TCSC, and SVC are converted into one hour, and the peak hour with the highest load demand is applied. The constriction factor-based particle swarm optimization (CF-PSO), Coot optimization algorithm (COOT), Equilibrium optimizer (EO), and Hippopotamus optimization algorithm (HO) are applied for the grid. The study cases are executed to choose the most suitable algorithm, and EO is selected. The results indicate that combining two TCSCs and two SVCs is the most effective in reaching the minimum total costs. In addition, the simulations for installing WPs in three different zones in Vietnam with different mean wind speeds, such as 6.33 m/s, 8.11 m/s and 10.25 m/s indicate that the mean wind speed of 6.33 m/s is not suitable for placing WPs. For the two remaining mean wind speeds, the investment of WPs is maximum, and the total cost is minimum.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-17847-9.

ABSTRACT This study investigates the spatial and temporal variations in tropospheric columnar NO₂ concentrations over thermal power plant (TPP) locations surrounding Delhi, utilizing data from the TROPOMI instrument on board the Sentinel-5P satellite. The data analysis analyses yearly, seasonal and monthly variability in NO₂ and influence by meteorological parameters from 2019 to 2024. Findings reveal significantly higher NO₂ concentrations during the pre-monsoon (3.1 × 10− 5 ± 2.1 × 10−6 mol m− 2) and winter (3.0 × 10− 5 ± 3.6 × 10−6 mol m− 2), with the lowest levels recorded during the monsoon (1.8 × 10− 5 ±1.43 × 10−6 mol m− 2), followed by the post-monsoon (2.1 × 10− 5 ±2.98 × 10−6 mol m− 2). Among the monitored TPPs, the Jajjar and Indira Gandhi plants consistently exhibited the highest annual NO₂ levels, likely due to their proximity to Delhi and additional emissions from urban sources. A significant decline in NO₂ concentrations was observed in 2020 during the COVID-19 lockdown, reflecting a temporary reduction in anthropogenic activities. However, levels rebounded post-2022, with a slight decrease in 2024, except during the Jajjar and Indira TPPs. Meteorological analysis indicates that wintertime inversions and low wind speeds contribute to pollution build-up, while high-magnitude monsoon winds enhance dispersion and can transport pollutants away from urban centres. Forward trajectory modelling using HYSPLIT reveals frequent air mass transport from NCTPP and Panipat TPPs towards Delhi. The other TPPs, such as Suaratgarh, Talwandi and Harduaganj, contribute significantly lower number of transport trajectories towards Delhi than others. This study illustrates the importance of satellite-based monitoring in evaluating NO₂ concentrations from TPPs and emphasizes the necessity for season-specific policy changes and strong emission regulations. This study approach is also valuable for monitoring NO₂ concentrations over other TPPs in India and the Globe and assessing their influence on the pollutant levels of the nearby cities.

Olfaction is essential for the survival and reproductive success of insects, mediating critical behaviors such as host plant location, mating, and oviposition. The fall webworm, Hyphantria cunea, is a highly polyphagous and invasive pest that infests a wide range of deciduous trees and shrubs. Its ability to efficiently locate suitable host plants primarily guided by olfactory cues, particularly during the larval stage, is a major contributor to its ecological success. While previous studies have examined the general olfactory and gustatory systems of H. cunea, the specific volatile compounds that elicit larval chemotactic responses remain largely uncharacterized. Behavioral and electrophysiological assays identified that the host plant volatile cis-3-hexenyl acetate (Z3HAc) functions as a chemoattractant for H. cunea larvae. Quantitative real-time polymerase chain reaction (PCR) and calcium imaging revealed that the antennal receptor HcOr3 is highly expressed and selectively responsive to Z3HAc. RNA interference-mediated knockdown of HcOr3 significantly reduced larval attraction to Z3HAc. Furthermore, single sensillum recordings confirmed that HcOr3 knockdown markedly decreased antennal sensitivity to Z3HAc, establishing its critical role in detecting this volatile compound. We identified Z3HAc as a key attractant in host plants for H. cunea larvae and demonstrated that the receptor HcOr3 likely mediates host plant recognition. This work enhances our understanding of the molecular basis of host-seeking behavior in this invasive pest and highlights HcOr3 as a promising target for species-specific pest control. Our findings elucidate the link between olfactory perception and chemotactic response to natural plant volatiles in folivorous insects. © 2025 Society of Chemical Industry.

Uncovering the location of photovoltaic power plants using heterogeneous remote sensing imagery

Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition.

This study aims to investigate the colonization and diversity of endophytic fungi in Amomum villosum roots across different planting locations and at various growth ages, and to analyze the fungal composition. In this study, we performed Illumina-based ITS sequencing to investigate the effects of growth ages and sample plots on the rhizosphere fungi of A. villosum. Colonization analysis revealed widespread colonization by arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSEs), but colonization rates did not increase significantly with plant age. Glomus was the dominant AMF genus, while Exophiala, Cladosporium and Cladophialophora dominated the DSE community. Beneficial fungi included Phoma, Acremonium, Myrothecium and Trichoderma. Alpha and beta diversity analyses indicated that fungal diversity, abundance, and community composition were significantly influenced by planting location but not by plant age. Collectively, planting location drives divergent root fungal communities in A. villosum, while plant age selectively affects specific taxa. AMF and DSEs were considered to be dominant beneficial microorganisms and were largely responsible for the growth and development of A. villosum. This study provides valuable insights for identifying beneficial endophytic fungi in A. villosum and optimizes the application of AMF and DSE agents in its ecological cultivation.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12866-025-04332-6.

Antipredator behaviors in animals often vary with developmental stage, microhabitat, and social context, yet few studies examine how these factors interact in species that undergo ontogenetic shifts in chemical defense. The spotted lanternfly (Lycorma delicatula) is an invasive planthopper whose nymphs transition from cryptically colored early instars to aposematically colored fourth instars that feed primarily on chemically defended host plants. We conducted 1460 simulated predator attacks on nymphs across four developmental stages to examine how antipredator behavior varies with instar, plant location (leaf vs. stem), host plant species, and local conspecific density. Nymphs exhibited three primary responses: hiding, sidestepping, or jumping. We found that location on the plant had the strongest effect, with nymphs on stems more likely to hide than those on leaves. Older instars were significantly less likely to hide and more likely to sidestep, particularly on stems, suggesting reduced reliance on energetically costly escape behaviors as chemical defenses accumulate. First instars were less likely to jump from their preferred host plant (tree of heaven) compared to other plant species. Higher local conspecific density reduced hiding probability, likely due to the dilution effect. These results demonstrate that antipredator strategies in L. delicatula are flexibly deployed based on developmental stage, microhabitat structure, and social context, with implications for understanding evolution of antipredator behavior in chemically protected species.

Environmental performance of sustainable supersulfated cements based on blast furnace slag: A life cycle study.

Abstract Lithium has been identified as an essential mineral to the economic and national security of the United States. It is vital for rechargeable batteries that surround us daily from the personal electronics to large‐scale energy storage. With a comprehensive techno‐economic analysis, the cost of battery‐grade lithium compounds production, i.e., lithium carbonate (LC) is evaluated and lithium hydroxide monohydrate (LHM), from both virgin (spodumene ore and brine) and recycled feedstocks (spent lithium‐ion batteries). The goal of this study is to inform about the economics of lithium compounds production with comprehensive insights into differences in manufacturing routes and pave a pathway to explore more innovative, domestic manufacturing processes in future for their cost competitiveness and environmental impact. The study includes details on mining and extraction operations as well as unit level operation in the refining process. Moreover, process level information has been collected for pyrometallurgical and hydrometallurgical battery recycling routes. This analysis shows that brine and direct lithium extraction are the cheapest pathways to produce LC or LHM (between $3.39 and 6.20 kg−1). The cost of production in the ore and recycling routes can range between $4.17 and $53.41 kg−1 and is highly depending on capital equipment investment, plant location, and the price of spodumene concentrate SC6.0.

The rapid population growth in Indonesia has significantly driven up energy demand, creating challenges in meeting these needs due to the declining availability of fossil fuels, the main source of power for most energy plants. Given Indonesia’s geographical position as a maritime nation with nearly six million square kilometers of ocean and over eighty-one thousand kilometers of coastline, the country has immense potential for harnessing ocean currents, waves, and tidal energy to develop renewable energy sources. Exploring these unconventional energy resources is crucial for meeting the country’s future energy demands. One of the most promising locations for Ocean Current Power Plants is the Sunda Strait. This research aims to model the ocean currents in the Sunda Strait, using Delft-3D software to assess its potential for Ocean Current Power plant development. The study also includes initial mapping of high-potential areas for energy production. Results from three observation points Sragi, Rimaubalak, and Sangiang indicate that the waters around Sangiang are most suitable for Ocean Current Power Plants with average currents of 0.86 m/s and a maximum of 4.77 m/s in 2023 also 0.938 m/s with maximum 5.20 m/s in 2024. For energy generation, the Gorlov turbine is particularly effective, producing 10642 kW in 2023 and 21285 kW in 2024, albeit with an efficiency of 36.52%. The Darrieus turbine showed a higher efficiency of 45.05%, though it generated slightly less power. Both turbines are deemed highly viable for further development in the Sunda Strait, offering a significant step toward Indonesia's renewable energy goals.

Molecular Modeling of Host Plant Location of Macrodactylus mexicanus1: Affinity of Odorant-Binding Proteins for Volatile Organic Compounds

This study anticipates cooperative manure management as a process for generating bioenergy from livestock manure, thereby reducing greenhouse gas (GHG) emissions in Bangladesh. Therefore, this study’s main objective was to identify clusters for cooperative society development and optimize suitable locations for biogas plant establishment within a cooperative system. Scenarios were explored based on manure types using cluster and network analyses of geographic information systems (GIS). The study observed 13 clusters, which have the potential to produce 6045 million m3 of biogas that can be converted to 9068.64 GWh of electricity yearly. Biogas plants additionally produced 5491.04 kilotons of biofertilizer by reducing GHG emissions estimated to be 10.16 million tons of CO2eq in 2024. This study also optimized 10, 6, and 8 optimum locations for biogas plants according to the scenarios. To implement the findings, this study recommended a coordinated action plan based on the circular economy, which helps to obtain both environmental and economic benefits for a cooperative society. These cooperatives can be implemented for renewable energy production from livestock manure at the community level for sustainable energy generation in Bangladesh.

ABSTRACT The purpose of community service activities in Ohoi (Village) Ngefuit, Kei Besar District, Southeast Maluku Regency, Maluku Province is to provide counseling and training on cultivating fruit plants in yards while explaining the benefits of fruit for health. Communities far from the city center are state assets that must be maintained, fostered, and trained to minimize the backwardness that occurs. Rambutan and longan have good adaptability, so they are easy to plant in the yards of farmer groups. The close planting location allows maximum care, so it is expected to provide good results. The process of sharing knowledge and mentoring in PkM activities is an important thing that academics due to the community as part of implementing science and knowledge. Key words: Community empowerment; counseling; training; fruit plants

This study aimed to investigate the impact of nine treatments on tomato plants and their planting location within a randomized complete block design. The treatments included gene silencing of the PL enzyme (RNAi-SlPL) and application of organic pectin and calcium to extend the marketability of tomato fruits and improve their quality. The RNAi-SlPL treatment outperformed others, revealed the lowest weight loss (41.4%) and highest firmness (4.89 kg·cm⁻²) after 36 days of cold storage, along with strong performance after 21 days at room storage (41.31% weight loss, 4.29 kg·cm⁻²). This was followed by CaP1P2 treatment, with 43.7% weight loss and 4.48 kg·cm⁻² firmness after 24 days of cold storage, and 45.87% weight loss with 4.15 kg·cm⁻² firmness after 14 days at room storage. Both treatments significantly boosted carotenoid (2.640 and 2.728 mg/100ml respectively) and lycopene (4.62 and 4.81 mg/100ml) levels post- cold storage. These findings highlight the effectiveness of RNAi technology and the use of organic pectin and calcium in extending the marketability of tomato fruits.

Nowadays, many countries in the world are taking action to reduce carbon dioxide emissions. One of the solutions is to promote energy-efficient and environmentally friendly transport systems of which a specific example is electric vehicles (EV) including cars, motorcycles and mopeds. The article analyses the global development of electric two-wheeled vehicles (ePTW) with China the world leader in the development of ePTW electromobility. China’s high position is due to both economic conditions (domestic raw material base, location of production plants) and top-down state support. Today, it is the financial support of programs promoting the purchase of electric motorcycles and mopeds that is the most important factor in increasing their sales. Electric mopeds have now become the fastest growing means of urban transport in many European countries influenced by electric bicycles being classified as e-mopeds.