Hasil untuk "Environmental effects of industries and plants"

Jiaqi Li

As a pervasive global challenge, carbon dioxide emissions are intrinsically related to energy consumption. However, the importance and spatiotemporal heterogeneity of the impact of various types of energy consumption, including clean energy and fossil fuels, on carbon emissions remain insufficiently investigated. Drawing on remote sensing data from 329 Chinese cities spanning 2005 to 2017, this study integrates SHAP-interpreted eXtreme Gradient Boosting with the Multiscale Geographically and Temporally Weighted Regression (MGTWR) model to elucidate the key contributors to carbon dioxide discharge and further investigate the spatiotemporal non-stationarity of the effect of energy consumption on carbon emissions. The results identify GDP, coal, oil, and electricity as key drivers of CO2 emissions, with a 1% GDP increase in developed regions raising emissions by up to 65%. Temporally, coal, natural gas, wind, and solar exerted short-term effects (under three years), nuclear power showed medium-term influence, while hydropower and oil exhibited persistence over a decade. Spatially, clean energy exhibited an east–west divergence, with the solar power’s emission-reduction coefficient in eastern regions being twice that in the west. These findings indicate that optimizing energy efficiency and fulfilling carbon reduction targets necessitate strategically tailored policies, which must be precisely aligned with the unique characteristics of specific regions and energy sources.

Pooja and Nisha Kumari

India has experienced periodic famines and droughts that have necessitated food imports. In 1950, the nation was experiencing a shortage of food grains due to the rapidly expanding population, which was placing increasing strain on the agricultural sector. The Green Revolution has contributed to a greater sense of self-assurance in our ability to produce food grains and maintain a balance between population growth and agricultural output. The output of rice and wheat, two important crops, has increased significantly as a result of the Green Revolution, which is its most notable achievement. The first Green Revolution had both positive and negative impacts on society and the environment. Despite the enormous amount of agricultural output, there are concerns regarding the nation’s level of food security. Emerging countries, such as India, have experienced gains in food production worldwide. Several notable negative repercussions of the Green Revolution emerged in the years that followed. Before the Green Revolution, its benefits and drawbacks were not the subject of any independent research. Following the Green Revolution, government activities caused the output of wheat and rice to quadruple, while local rice types and millets experienced a decline in productivity. Consequently, several local crops have perished and are no longer cultivated.

Hala Alrawashdeh, Laila A. Al-Khatib and Bassam Abed

The aviation industry plays a crucial role in global connectivity and transportation. However, its environmental footprint continues to grow alongside the expanding popularity of aviation. By analyzing a decade-long dataset, the novelty of this research lies in delving into the relationship between aircraft age and major aviation emissions, such as hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx), during landing and take-off (LTO) operation using advanced machine learning algorithms. The analysis of this research comprises three horizons. Firstly, an inventory of aircraft emissions was constructed by analyzing aircraft fleet data at Queen Alia International Airport (QAIA) in Jordan. Secondly, the correlation between these emissions and aircraft age was rigorously examined. Finally, predictive models for aircraft age were developed based on pollutant emission features using advanced machine learning algorithms. The findings of the study revealed a discernible impact of aircraft age on emissions, underscoring the importance of considering the aging factor in assessing the environmental implications of aviation. The machine learning models exhibited a capacity to forecast pollutant emissions with a notable degree of accuracy, with a Mean Squared Error (MSE) of about 3.0931. This offers valuable perspectives that can enhance comprehension of aviation’s environmental footprint.

Abhay Yadav, Divya Srivastava and Vivek Mathur

Air pollution is the atmospheric state in which the concentration of specific elements has adverse impacts on human health as well as the environment, including global warming, transportation disruptions, acid rain, and ozone layer depletion. Nowadays, a large portion of the world’s population lives in urban areas, where population growth and the increasing number of vehicles have significantly worsened air quality. Clean air is essential for the health and well-being of any region’s environment and its inhabitants. Henceforth, the primary focus of this research endeavor is to meticulously scrutinize the levels of key air pollutants, notably nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), leveraging satellite remote sensing data obtained from TROPOMI EXPLORER across a network of monitoring stations dispersed throughout Lucknow City. Additionally, it aims to meticulously dissect groundbased air quality monitoring data to validate and amalgamate the observations derived from satellite technology. Furthermore, it analyzes the distribution of concentrations of primary air pollutants, encompassing NO₂, SO₂, and PM₁₀, within Lucknow City, juxtaposing them against the stringent benchmarks stipulated by the World Health Organization (WHO) for air quality standards. Moreover, it endeavors to ascertain the deleterious health ramifications of air pollution by correlating air quality metrics with health outcomes among the denizens of Lucknow City through a meticulously crafted questionnaire survey. The scrutiny of satellite imagery unveiled a conspicuous escalation in the concentration of air pollution parameters vis-à-vis the WHO’s prescribed thresholds, portending consequential adverse ramifications for both the environment and human health.

Michèle Gaillard, Emma Saint-Preux, Amélie Deglaire et al.

Infant formulas provide the necessary nutrients to infants aged 0–6 months whenever breastfeeding is restrained. Their production must balance nutritional quality, environmental sustainability and economic profitability in the manufacturing process. Traditional heat treatment processes, such as pasteurization, ensure microbiological safety but lead to protein denaturation and Maillard reaction, thereby diminishing the nutritional quality of the product. New processes involve low-heat sanitation using membrane microfiltration to also maintain protein quality. Life Cycle Assessment was used to compare the potential environmental impacts of the production of infant formula powder via the classic route (using pasteurization) or the alternative route (using microfiltration) at a semi-industrial scale. No matter the sanitation procedure, the production of milk and oil ingredients exhibited the largest contribution to impacts, followed by evaporation and spray-drying, i.e. unit operations with energy-consuming water evaporation. Closer insight on sanitation operations revealed that while pasteurization and microfiltration are comparable across various impact categories, microfiltration demands significant water and detergent for cleaning, whereas pasteurization is energy-intensive during its steady-state phase. Although energy consumption is reduced, 1 kg of infant formula produced through the alternative route emits 11.1 kg of CO2 equivalent, against 10.4 for the classic route. The higher impact of the alternative route on climate change, as well as on other agriculture-related impact categories, is primarily attributed to the increased demand for skim milk to implement microfiltration. Sensitivity analyses revealed strategies to reduce infant formula's environmental impacts, such as using liquid ingredients to avoid drying or increasing pre-evaporation dry matter to save energy.

Ali Mohsen Mohammed, Aalaa Fahim Abbas and Haider Mashkoor Hussein

Ibuprofen (IBU) is increasingly recognized as a significant category of emerging micropollutants that infiltrate aquatic ecosystems. IBU possesses a significant capacity to inflict ecological harm, adversely affecting both ecosystems and the health of humans and animals. The primary contributors to the environmental presence of IBU encompass the pharmaceutical manufacturing sector, wastewater treatment plants (WWTPs), hospital effluents, and agricultural byproducts. The degradation of IBU is contingent upon various factors, including its chemical and biological persistence, physicochemical properties, and the methodologies employed for its treatment. A multitude of techniques has been employed to mitigate its detrimental effects, involve adsorption, coagulation, bioremediation (constructed wetlands (CWs), membrane bioreactors (MBRs), microalgal-based systems), advanced oxidation processes (AOPs), membrane filtration systems (including reverse osmosis, nanofiltration, and microfiltration), as well as photocatalytic methods, among others. The exploration of more innovative and effective technologies aimed at IBU degradation necessitates a thorough investigation and should be specifically tailored for cost-efficiency and scalability. Additionally, the assessment of green and eco-friendly alternatives for IBU, characterized by attributes such as negligible bioaccumulation, minimal persistence, environmental compatibility, and low or no toxicity, is equally essential. Bacterial degradation mechanisms constitute a highly promising alternative for the biodegradation of IBU, especially through the application of meticulously chosen strains that have been isolated from contaminated environments.

Vevosa Nakro, Ketiyala Ao, Tsenbeni N. Lotha, Imkongyanger Ao, Lemzila Rudithongru, Chubaakum Pongener, Merangmenla Aier, Aola Supong and Latonglila Jamir

The discharge of large quantities of organic dyes into the environment causes significant harm to humans and the environment. Thus, there is an urgent need to develop cost-effective adsorbents for removing these dyes. In the present study, the synthesis of activated carbon (AC) derived from mixed fish scale waste using KOH activation was investigated for Congo red (CR) dye removal. The finding shows that the obtained biocarbon has a fixed carbon of 42.9% with a crystallinity index of 15.01%. N2 adsorption-desorption isotherm was found to be type IV, signifying mesoporous structure with a surface area and total pore volume of 150.049 m2 g-1 and 0.119 cm3.g-1. Batch adsorption was carried out by various adsorbent doses, initial concentration, contact time, and pH to comprehend the effect of operating parameters on its removal efficacy. The isotherm studies fitted well for Freundlich with an R2 of 0.99%. Adsorption kinetics was best fitted by the pseudo-second-order model and thermodynamic studies revealed the adsorption process to be exothermic and spontaneous. The efficiency of AC was also studied by an amount of sorption and desorption cycles which showed its potential for reusability up to the sixth cycle. Thus, the findings suggest that activated carbon derived from mixed fish scale waste is a promising adsorbent for removing Congo red dye from aqueous solutions.

Ofek Aloni, Gal Perelman, Barak Fishbain

Synthetic datasets are widely used in many applications, such as missing data imputation, examining non-stationary scenarios, in simulations, training data-driven models, and analyzing system robustness. Typically, synthetic data are based on historical data obtained from the observed system. The data needs to represent a specific behavior of the system, yet be new and diverse enough so that the system is challenged with a broad range of inputs. This paper presents a method, based on discrete Fourier transform, for generating synthetic time series with similar statistical moments for any given signal. The suggested method makes it possible to control the level of similarity between the given signal and the generated synthetic signals. Proof shows analytically that this method preserves the first two statistical moments of the input signal, and its autocorrelation function. The method is compared to known methods, ARMA, GAN, and CoSMoS. A large variety of environmental datasets with different temporal resolutions, and from different domains are used, testing the generality and flexibility of the method. A Python library implementing this method is made available as open-source software.

Shujie Wu, Mindy Mallory, Teresa Serra

We investigate the impact of the policy-driven expansion of the diesel and renewable diesel industry on local soybean prices. Because soybean oil is a key feedstock for biodiesel and renewable diesel, significant investments have been made in new soybean crush facilities and the expansion of existing ones. We quantify the effect of both new and existing soybean plants on soybean basis using panel data and a differences-in-difference approach. The data available on new plants does not allow us to identify any statistically significant impacts. However, existing plants increase the basis by 23.36 to 9.20 cents per bushel, with the effect diminishing with distance. These results suggest the relevance of biofuel policies in supporting rural economies and have relevant policy implications.

Alper Saricioglu, Mujde Erol Genevois, Michele Cedolin

The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics.

Adam J Riesselman, Evan M Cofer, Therese LaRue et al.

Quantifying organism-level phenotypes, such as growth dynamics and biomass accumulation, is fundamental to understanding agronomic traits and optimizing crop production. However, quality growing data of plants at scale is difficult to generate. Here we use a mobile robotic platform to capture high-resolution environmental sensing and phenotyping measurements of a large-scale hydroponic leafy greens system. We describe a self-supervised modeling approach to build a map from observed growing data to the entire plant growth trajectory. We demonstrate our approach by forecasting future plant height and harvest mass of crops in this system. This approach represents a significant advance in combining robotic automation and machine learning, as well as providing actionable insights for agronomic research and operational efficiency.

Corentin Fivet, Catherine De Wolf, Thibaut Menny et al.

Load-bearing systems in buildings, significant in material use and embodied greenhouse gas emissions (EGHGE), have lacked detailed analysis on their environmental and functional relationships over time and space. This study evaluates the environmental impacts of building structures in Geneva, Switzerland, considering factors like material usage, EGHGE, and urban development. A new method using a similarity-weighted function projects environmental impacts onto a GIS-based building stock, analysing 48 archetypal and 84,477 stock buildings built from 1850 to 2018. Results show a 37% reduction in structural volume per floor area and a 10% increase in mass over time. Buildings predating the masonry-to-concrete transition would produce 7% more EGHGE if constructed today. Multi-residential buildings emit 14% less EGHGE than single homes. A new indicator amortizes upfront environmental effects over a building's lifespan, aiding in historical comparisons of building stocks. This approach underscores the need for spatial-temporal environmental impact mapping to understand sustainable urban development dynamics.

Giuseppe Modica, Angelo Pulvirenti, Daniela Spina et al.

Sicily ranks as the third-largest region in Italy for olive growing and olive oil production, holding the second position nationally regarding the number of active olive oil mills. This pioneering study integrates spatial and economic analyses to examine the geographical distribution of olive oil mills in Sicily and their relationship with the localization of olive groves. Using Local Indicators of Spatial Association (LISA), we conducted an advanced analysis of spatial patterns of olive oil mills, considering travel time on the road network. The adopted methodology addresses issues related to overestimation based on straight-line assumptions and the neglect of travel speed. Unlike traditional Euclidean distance approaches, our methodology provides a detailed understanding of the spatial relationships between olive oil mills and olive groves, revealing distinct patterns linked to elevation and proximity to olive groves. By linking profitability indicators with spatial clusters, we identify different thresholds of economic sustainability. Consequently, these findings contribute to a more comprehensive understanding of the olive oil industry, suggesting more environmentally sustainable practices. Policymakers, researchers, and industry stakeholders can leverage this knowledge to make informed decisions that foster the long-term sustainability of the olive oil sector.

Busthan Busthan, Hendra Pachri, Ilham Alimuddin et al.

Analysis of landslide slip surface based on the engineering properties of tuff rocks, including shear strength, water content, and infiltration rate on the West Sinjai road section, South Sulawesi, Indonesia. This study aimed to analyze the effect of shear strength, water content infiltration rate, and weathering grade of tufa rocks in the study area. The methods used in this study area included a study of weathered tuff profile characteristics, weathered tuff infiltration testing and residual soil (RS) determination of weathered tuff water content and residual soil, and testing of shear strength of weathered tuff and residual soil. This research used aspects of engineering geology, including shear strength, water content, and infiltration rate, as well as rock weathering grade to determine the slip surface in tuff to determine the volume of landslide material. The results showed that the tuff profile consisted of four grades, namely moderately weathered tuff (MW), highly weathered tuff (HW), completely weathered tuff (CW), and soil residual (RS). The rate of tuff infiltration increases with increasing weathering grade. The water content is more significant with the high degree of weathering of tuff. At the same time, the shear strength decreases with high weathering. Therefore, the research area is prone to landslide events. The slip surface is in a layer of moderately weathered tuff rock (MW), and those that experience landslides are highly weathered tuff rock (HW), completely weathered (CW), and residual soil (RS).

Marx Herrera-Machaca, Carlos Ancco-Mamani, Gabriel Alarcon Aguirre et al.

Gold mining has been causing the most severe impacts on the soils of the Peruvian Amazon. It has created challenges for their recovery. In this context, soil amendments could play a crucial role in plant establishment in post-mining soils. The study aimed to analyze the effects of two amendments on the early plant survival and growth of seven species in the reclamation of severely degraded lands by gold mining in the Southeastern Peruvian Amazon. The study was based on a completely randomized block design, including 2-amendment treatments (T1: sawdust + island guano manure and T2: T1 + organic soil + hydrogel) and a control. The plant survivorship, height growth, diameter growth, and biomass accumulation were measured. This study found that amendments may be effective at increasing survivorship and plant growth in degraded lands by gold mining in the Peruvian Amazon. The amendments increased the survival, diameter, height, and biomass of most plant species in the study. In general, survivorship and plant growth in T2 were high compared to T1. At the end of the experiment, the highest survivorship was for an Indigofera suffruticosa and Crotalaria pallida (>80%). The diameter growth was higher in T2 than in T1. The species growing fastest in diameter (>1.5 cm) were Crotalaria cajanifolia, C. pallida and Ochroma pyramidale. Soil amendments provided similar effects on height for most species except for I. suffruticosa. Therefore, C. pallida, I. suffruticosa, C. cajanifolia and O. pyramidale are key species to be considered in reforestation and/or restoration initiatives, due to its potential to acclimate and establish itself in severely degraded areas.

Fan Zhao, Yijia Chen, Dianhan Xi et al.

Hermit crabs play a crucial role in coastal ecosystems by dispersing seeds, cleaning up debris, and disturbing soil. They serve as vital indicators of marine environmental health, responding to climate change and pollution. Traditional survey methods, like quadrat sampling, are labor-intensive, time-consuming, and environmentally dependent. This study presents an innovative approach combining UAV-based remote sensing with Super-Resolution Reconstruction (SRR) and the CRAB-YOLO detection network, a modification of YOLOv8s, to monitor hermit crabs. SRR enhances image quality by addressing issues such as motion blur and insufficient resolution, significantly improving detection accuracy over conventional low-resolution fuzzy images. The CRAB-YOLO network integrates three improvements for detection accuracy, hermit crab characteristics, and computational efficiency, achieving state-of-the-art (SOTA) performance compared to other mainstream detection models. The RDN networks demonstrated the best image reconstruction performance, and CRAB-YOLO achieved a mean average precision (mAP) of 69.5% on the SRR test set, a 40% improvement over the conventional Bicubic method with a magnification factor of 4. These results indicate that the proposed method is effective in detecting hermit crabs, offering a cost-effective and automated solution for extensive hermit crab monitoring, thereby aiding coastal benthos conservation.

Albert J. Zhai, Xinlei Wang, Kaiyuan Li et al.

The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to recover complete shapes of plants due to heavy occlusion and complex geometries. In this work, we present a novel method for 3D modeling of agricultural crops based on optimizing a parametric model of plant morphology via inverse procedural modeling. Our method first estimates depth maps by fitting a neural radiance field and then optimizes a specialized loss to estimate morphological parameters that result in consistent depth renderings. The resulting 3D model is complete and biologically plausible. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructed canopies can be used for a variety of monitoring and simulation applications.

Bryanne McDonough, Olivia Curtis, Tereasa Brainerd

We present radial profiles of luminosity-weighted age, $age_L$, and $ΔΣ_{SFR}$ for various populations of high- and low- mass central and satellite galaxies in the TNG100 cosmological simulation. Using these profiles, we investigate the impact of intrinsic and environmental factors on the radial distribution of star formation. For both central galaxies and satellites, we investigate the effects of black hole mass, cumulative AGN feedback energy, morphology, halo mass, and local galaxy overdensity on the profiles. In addition, we investigate the dependence of radial profiles of the satellite galaxies as a function of the redshifts at which they joined their hosts, as well as the net change in star-forming gas mass since the satellites joined their host. We find that high-mass ($M_*>10^{10.5} M_{\odot}$) central and satellite galaxies show evidence of inside-out quenching driven by AGN feedback. Effects from environmental processes only become apparent in averaged profiles at extreme halo masses and local overdensities. We find that the dominant quenching process for low-mass galaxies ($M_*<10^{10} M_{\odot}$) is environmental, generally occurring at low halo mass and high local galaxy overdensity for low-mass central galaxies and at high host halo masses for low-mass satellite galaxies. Overall, we find that environmental processes generally drive quenching from the outside-in.

Sheela Upendra and Jasneet Kaur

Due to its detrimental effects, notably on the well-being and biota of the ocean, microplastic contamination is becoming a bigger concern. Because of this, the issue of microplastics in the marine ecosystem is currently a major concern. The purpose of the study is to objectively evaluate the most recent data supporting the impact of microplastic contamination in seawater. When creating the standards for assessing the literature, P.I.C.O. was taken into account. For this inquiry, databases were selected and used throughout the data-collecting process. We checked PubMed, CINAHL, Google, Hinari, and the Cochrane Library. Boolean operators (AND, OR) and keywords were employed in the search to avoid oversaturating the data. Keywords used as per MeSH: Microplastic, plastics, seawater, ocean, pollution, microplastic exposure. The last five years (Since 2017) worth of studies were incorporated. Boolean search for relevant terms used. This limited my query to 188 records through various database searches. Several things were removed because they were unrelated to the study’s subject. Due to its detrimental impact on marine biota, the issue of microplastic contamination in the marine ecosystem is a current concern. Microplastics, which serve as a vector, become stuck with harmful pollutants. It is necessary to implement conservation management strategies and assistance for different educational programs to protect the environment from these hazardous microplastics. Humans are exposed to plastic waste when eating fish tainted with plastic. As a result, there are various outbreaks of chronic diseases, and people suffer the effects. The public’s education on the harmful effects of microplastics is a crucial need in this field. As a result, many inventions would be promoted to decrease the use and consumption of plastic and its products.

Rey Cesar Olorvida, Rey Ann Bande, Dennis Ngalot et al.

Due to the global problem of electronic wastes (e-wastes), the concept of a circular economy is deemed a viable framework for achieving minimal resource extraction and waste generation. Despite the advances in the literature on managing the circularity of e-wastes, the consumption aspect needs much attention. Although some barriers to the consumption of recovered e-wastes, such as refurbished mobile phones, were already identified, an overarching evaluation of these barriers remains a gap. Thus, this work evaluates these relevant barriers by considering their internal strengths (or priorities) and the intertwined relationships between them under uncertainty. In achieving this agenda, three phases of the methodology were implemented: (1) performing a systematic literature review of the barriers, (2) finalizing the list of barriers following a focus group discussion, and (3) evaluating the barriers using a soft system model. The intricacies of the evaluation process prompt the development of a proposed integration of grey system theory and the weighted influence non-linear gauge systems (WINGS), coined grey WINGS. Findings from a case study in the Philippines reveal that negative perception, inferior quality, the misconception of the refurbishment concept, technological obsolescence, and lack of awareness are the critical barriers to the consumption of refurbished mobile phones. Discussions behind these barriers in view of consumer decisions were offered. Also, some policy insights were outlined to overcome these barriers. In effect, this work contributes twofold to the literature: (1) evaluating the barriers to the consumption of refurbished mobile phones to help design circularity initiatives, and (2) methodologically, integrating grey system theory within the framework of the WINGS method to address uncertainty in judgment elicitations.