Hasil untuk "Environmental technology. Sanitary engineering"

Sergi Garcia-Segura, Mariana Lanzarini-Lopes, Kiril D. Hristovski et al.

Abstract Nitrate contamination in surface and ground waters is one of this century’s major engineering challenges due to negative environmental impacts and the risk to human health in drinking water. Electrochemical reduction is a promising water treatment technology to manage nitrate in drinking water. This critical review describes the fundamental principles necessary to understand electrochemical reduction technologies and how to apply them. The focus is on electrochemical nitrate reduction mechanisms and pathways that form undesirable products (nitrite, ammonium) or the more desirable product (dinitrogen). Factors influencing the conversion rates and selectivity of electrochemical nitrate reduction, such as electrode material and operating parameters, are also described. Finally, the applicability for treating drinking water matrices using electrochemical processes is analyzed, including existing implementation of commercial treatment systems. Overall, this critical review contributes to the understanding of the potential applications and constraints of electrochemical reduction to manage nitrate in drinking waters and highlights directions for future research required for implementation.

I. Angelidaki, M. Alves, D. Bolzonella et al.

Qirui Wu, Jinfeng Liu, Xiaohong Wang et al.

The organ-on-a-chip (OOAC) is in the list of top 10 emerging technologies and refers to a physiological organ biomimetic system built on a microfluidic chip. Through a combination of cell biology, engineering, and biomaterial technology, the microenvironment of the chip simulates that of the organ in terms of tissue interfaces and mechanical stimulation. This reflects the structural and functional characteristics of human tissue and can predict response to an array of stimuli including drug responses and environmental effects. OOAC has broad applications in precision medicine and biological defense strategies. Here, we introduce the concepts of OOAC and review its application to the construction of physiological models, drug development, and toxicology from the perspective of different organs. We further discuss existing challenges and provide future perspectives for its application.

Tiezheng Tong, Lonqian Xu, Thomas Horseman et al.

G. Satasiya, Madhava Anil Kumar, S. Ray

Biofouling is a common phenomenon caused by waterborne organisms such as bacteria, diatoms, mussels, barnacles, algae, etc., accumulating on the surfaces of engineering structures submerged under water. This leads to corrosion of such surfaces and decreases their moving efficiency. Conventional antifouling agents are synthetic chemicals which are hazardous to non-target species. Further, these agents are mixed with paints, releasing toxins in the water bodies that affect other organisms. Thus, the development of natural alternatives for anti-fouling chemicals is urgently needed. This review examines the development of environmentally friendly antifouling technologies, focusing on the switch from biocidal coatings that leach toxic elements like mercury and copper to sustainable substitutes such as hybrid, biomimetic, and nanotechnology-based antifouling solutions. Research also focuses on increasing antifouling properties and reducing environmental impact by incorporating natural antifouling agents and constructing hybrid coatings that include multiple technologies. The financial effects of implementing these new technologies compared to more conventional approaches highlight the significance of sustainable practices in the maritime industry. This thorough review sheds light on the state of antifouling technology. It recommends future research to maximize ecological compatibility and apply these advancements to broader applications.

Bruna Santos, Filomena Freitas, Abílio J. F. N. Sobral et al.

ABSTRACT The growing environmental crises demands an urgent transition from a linear to a circular economy. Microalgae are photosynthetic microorganisms that offer exceptional potential due to their rapid growth, high CO₂ fixation capacity, and ability to remove nutrients and pollutants from wastewater, producing both clean water and valuable biomass. Such characteristics have attracted interest in developing circular systems that transform wastes into resources such as biomaterials, biofertilisers, biofuels and bioactive compounds. However, various challenges hinder their industrial application, including technical, economic, environmental, commercial and political barriers. Technical limitations such as inefficient culture systems, low productivity and contamination risks, can be addressed by using genetic engineering tools to develop superior strains, and by developing bioreactors coupled with emerging technologies (AI, Digital Twin). Additionally, it was found that studies using wastewater for microalgae cultivation and a biorefinery approach to recover low and high value bioproducts were found to be energetically, environmentally and economically viable. Several projects and studies demonstrating microalgae-based circular economy models were highlighted. Finally, the implementation of clear regulations and guidelines for wastewater composition in microalgae systems is recommended to facilitate market acceptance and consumer trust in microalgae-derived products.

Syed Ali Reza, Md Sakibul Hasan, Mohammad Hamid Hasan Amjad et al.

As urbanization continues to expand and evolve in the USA, power demand has increased manifold, and with it has arisen significant environmental problems in the form of increased greenhouse gas emissions and loss of resources. In this paper, we explore how future machine-learning techniques could predict power consumption in U.S. cities. The central aim of this research is to develop advanced machine learning models with the potential to effectively predict energy consumption in cities. This involves not only identifying the key variables behind energy consumption but also selecting and fine-tuning machine learning algorithms that are most capable of understanding the dynamics of urban energy intricacies. This study focuses on the energy consumption patterns in the large cities of the United States, recognizing the diversity of challenges and opportunities presented by different geographic and demographic situations. The dataset used in this research project offered a comprehensive view of energy consumption across various fields of household, commercial, and industrial consumption, giving a holistic view of energy dynamics within cities. It integrated data collected from smart meters that offer granular electricity consumption patterns at the level of individual households and businesses with weather reports that detail ambient conditions governing energy demand, such as temperature and humidity fluctuations. Government energy records add historical context and policy information, further enhancing the dataset and enabling close analysis of trends and patterns in energy consumption. The next phase was to select and train three distinct machine models to explore the energy consumption dataset, namely, Logistic Regression, Random Forest, and XG-Boost algorithms. Random Forest outperformed Logistic Regression and XG-Boost slightly in terms of accuracy and other evaluation metrics. However, all models exhibit relatively low accuracy, suggesting the need for further tuning, feature engineering, or alternative models to improve predictions. In major cities in the U.S. such as Los Angeles, Chicago, and New York, smart power forecasting based on AI is revolutionizing power distribution and power planning in cities. By utilizing advanced machine learning models, these cities can process vast amounts of information and predict power usage with high accuracy. The incorporation of artificial intelligence (AI) in urban power planning has been a defining feature of modern-day power management in the USA. Major cities such as Los Angeles, Chicago, and New York are increasingly adopting AI-powered power forecasting technologies to rationalize power distribution. The integration of machine learning insights in U.S. government-driven green construction is instrumental in driving sustainable construction in infrastructure. By utilizing data-driven approaches, policymakers are in a position to identify the optimal design methods and low-power technologies with high performance in buildings.

Xuemei Zhu, Chao Yang, Qing Du et al.

Per/polyfluoroalkyl substances (PFASs), characterized by their ultrastable C-F bonds and pervasive environmental persistence, present critical remediation challenges due to their recalcitrance and bioaccumulative potential. Conventional oxidative degradation methods predominantly yield bioaccumulative short-chain fluorinated derivatives, failing to achieve molecular annihilation. We highlight reductive defluorination as a transformative strategy to directly cleave C-F bonds and mitigate toxicity through two distinct electron-transfer modalities: indirect routes mediated by reactive species (e.g., hydrated electrons/active hydrogen) contrasted with direct mechanisms employing biocatalytic or electrochemical systems for targeted electron injection. Mechanistic taxonomy and quantitative structure-reactivity analyses reveal that defluorination efficiency is governed by the molecular architecture (e.g., chain length and fluorination patterns) and operational parameters (e.g., pH, redox potentials, and solution matrices). While standalone reductive technologies face scalability constraints from energy intensity and secondary contamination risks, synergistic integration of bioremediation-electrochemical-photocatalytic systems demonstrates enhanced defluorination efficiency. By coupling molecular-level degradation mechanisms with modular engineering, we propose future directions for developing reductive defluorination, offering a sustainable pathway to eliminate environmental recalcitrance and comply with evolving global water quality mandates.

S. Choi, Mi Na Rhie, Hee Taek Kim et al.

As concerns increase regarding sustainable industries and environmental pollutions caused by the accumulation of non-degradable plastic wastes, bio-based polymers, particularly biodegradable plastics, have attracted considerable attention as potential candidates for solving these problems by substituting petroleum-based plastics. Among these candidates, polyhydroxyalkanoates (PHAs), natural polyesters that are synthesized and accumulated in a range of microorganisms, are considered as promising biopolymers since they have biocompatibility, biodegradability, and material properties similar to those of commodity plastics. Accordingly, substantial efforts have been made to gain a better understanding of mechanisms related to the biosynthesis and properties of PHAs and to develop natural and recombinant microorganisms that can efficiently produce PHAs comprising desired monomers with high titer and productivity for industrial applications. Recent advances in biotechnology, including those related to evolutionary engineering, synthetic biology, and systems biology, can provide efficient and effective tools and strategies that reduce time, labor, and costs to develop microbial platform strains that produce desired chemicals and materials. Adopting these technologies in a systematic manner has enabled microbial fermentative production of non-natural polyesters such as poly(lactate) [PLA], poly(lactate-co-glycolate) [PLGA], and even polyesters consisting of aromatic monomers from renewable biomass-derived carbohydrates, which can be widely used in current chemical industries. In this review, we present an overview of strain development for the production of various important natural PHAs, which will give the reader an insight into the recent advances and provide indicators for the future direction of engineering microorganisms as plastic cell factories. On the basis of our current understanding of PHA biosynthesis systems, we discuss recent advances in the approaches adopted for strain development in the production of non-natural polyesters, notably 2-hydroxycarboxylic acid-containing polymers, with particular reference to systems metabolic engineering strategies.

E. Mohana priya, E. Uma, Debasis Mitra et al.

Industrial effluents and wastewater containing elevated levels of heavy metals pose significant threats to water quality and groundwater, potentially leading to severe health risks.The physicochemical parameters and concentrations of heavy metals, including Cadmium (Cd), Chromium (Cr), Lead (Pb), Nickel (Ni), and Zinc (Zn), along with pollution indices of well water,were analyzed using standard procedures. The water samples exhibited alkaline properties and high electrical conductivity withheavy metal concentrations of 0.47 ± 0.07 mg/Lfor Cd, 0.038 ± 0.01 mg/Lfor Pb, 0.10 ± 0.02 mg/Lfor Ni, and 0.58 ± 0.05 mg/Lfor Zn, while Cr was below detection limits. The average Geoaccumulation index values for Cd, Ni, and Pb were 2.657, 1.763, and 1.287, respectively, whereas the Heavy Metal Pollution index exceeded 100 for all samples. The contamination factor values followed the order of Cd > Ni > Pb > Zn, and all samples had a Pollution Load Index above 1. The results indicated that Cd is a moderately strong pollutant, whereas Ni and Pb are moderate pollutants. This study provides a crucial baseline for assessing the migration of heavy metals from well water to vegetables, and ultimately to humans.

Waqed H. Hassan, Karrar Mahdi, Zahraa K. Kadhim

S. A. Ushakov, V. Y. Kumaritov

The presence of odorous pollutants in the atmospheric air is increasingly considered as a significant environmental and sanitary problem, since persistent odors contribute to an increase in the number of complaints and negatively affect the comfort of life of the population, however, direct measurement of concentrations of individual substances often does not reliably assess the strength of the odor. The lack of a unified regulatory system in Russia makes the problem particularly relevant, which leads to the use of disparate and not always quantitative control methods. The purpose of the work is to generalize and compare the existing approaches to the definition of odors used in Russia and abroad. The article discusses in detail three main groups of methods. Instrumental methods such as gas chromatography-mass spectrometry provide accurate identification of the chemical composition, but do not reflect the subjective perception of odor by humans and are often unable to detect ultra-low concentrations of odorants. Sensory methods, the key of which is olfactometry, directly measure the threshold of olfactory perception, providing a quantitative assessment in units of smell (EC/m3), however, they are subjective, time-consuming and require the involvement of trained experts. Promising sensor systems, the so-called «electronic noses», allow for real-time operational monitoring, but so far they suffer from instability and the need for complex calibration. Auxiliary approaches, including bioindication and variance modeling, complement the overall picture, but do not provide direct quantitative estimates. The analysis demonstrates that none of the methods is universal. As the main conclusion, the authors emphasize the need for an integrated approach that integrates instrumental, sensory methods and modeling to obtain a reliable assessment of odor pollution. In the future, the development of technologies, including the use of artificial intelligence, and the formation of an adequate regulatory framework in Russia will make it possible to effectively address the challenges of controlling and reducing the impact of unpleasant odors on the population.

R. Krishankumar, Fatih Ecer, A. Mishra et al.

Sustainable transport in cities has been gaining a lot of attraction recently and a core focus of engineering management. Internet of Things (IoT) is seen as a widely accepted technology that promotes sustainability through the interconnection of diverse computing sources for solving environmental problems. Previous studies on IoT have discussed interesting factors toward its adoption, but selecting a suitable IoT service provider (IoTSP) is an open challenge due to a diverse set of factors in practice. Driven by the challenge, in this article, a generalized fuzzy-based decision model is put forward for IoTSP selection, which is the prime objective of the study. Initially, a strength, weakness, opportunity, threat (SWOT) analysis is adopted to identify the crucial challenges in IoT adoption. Later, the relative significance of these challenges is calculated by adopting the regret/rejoice approach. Due to uncertainty, certain rating information of IoTSPs is missing that are rationally imputed by proposing an analytical approach. Rating matrices from experts are transformed into opinion vectors, and a prioritization algorithm is developed with query vector for rational personalized ordering of IoTSPs. Data for the study are acquired via questionnaire, which is filled by experts. The efficacy of the developed model is exemplified by using a real case study of IoTSP selection for pollution management in Chennai. Concerning the findings, mobility, security, and connectivity are the most vital factors for IoTSP selection. Results show that the proposed model is a viable tool for IoTSP selection and it is robust, unique, and stable compared to its counterparts.

Shiqi Wang, Zhongyuan Yuan, Nanyang Yu

Organic Rankine cycle (ORC) has been considered as one of the most promising technologies in industrial waste heat utilization and power generation. During the actual operation of ORC system, due to the fluctuation of cooling and heat sources, the system operates under off-design conditions in most cases. In this paper, thermodynamic model, heat transfer process description and power equipment model are established to evaluate the operating parameters of ORC for the off-design conditions. Evaporation temperature and condensation temperature are taken as independent parameters for the operation of ORC system. Genetic algorithm is adopted to optimize the independent parameters under the maximum net output power. The results show that the effect of optimizing independent parameters is to make the working fluid at the outlet of the preheater as close as possible to a saturated liquid state, and the working fluid at the inlet of the screw expander should be in a saturated gas state. With the optimal power output increasing by 19.1% for every 5 °C increase in hot water inlet temperature, 9.2% for every 20 kg/s increase in hot water mass flow rate, and 3.9% for every 1 °C decrease in cooling water temperature. The optimization method of off-design operating conditions has good system performance and good engineering application prospects.

Liudmyla Datsenko, Svitlana Titova, Marharyta Dubnytska

Aim of the study: The purpose of the study is to substantiate the conceptual approaches of the complex and to consider its main components regarding the restoration of territories affected by the war in Ukraine, with an emphasis on the incompleteness of current legal acts. It is therefore essential to develop new legal mechanisms that will ensure the procedure for removing contaminated lands into state ownership for their long-term restoration, with appropriate compensation to landowners for the period of time that the contaminated land remains in state ownership. Material and methods: The theoretical basis consists of academic research by domestic and international scientists in the field of land management and environmental protection, legislative and regulatory acts, methodological and instructional materials, statistical and analytical data of ministries and departments of Ukraine, as well as public organizations regarding the use of land resources and socio-economic development of the regions of Ukraine. Methods used include: monographic analysis; synthesis method; structural and logical method; systemic approach; dialectical principle of connection /interaction. Results and conclusions: The land relations during the reconstruction of Ukraine should be based on the following principles and approaches: openness of the public cadastral map of Ukraine; simplification of permit procedures; assessment of land and soil quality, inventory; continuation of the trend of decreasing arable land; conservation of lands, the use of which could harm human life and health as well as the state of the environment; expropriation of land from tenants who are connected to Russia or Belarus; soil conservation in the context of war; introduction of the state system for the control of land resources and the responsibility of land users.

Yusuke Sasaki, Y. Yoshikuni

Marine macroalgae have huge potential as feedstocks for production of a wide spectrum of chemicals used in biofuels, biomaterials, and bioactive compounds. Harnessing macroalgae in these ways could promote wellbeing for people while mitigating climate change and environmental destruction linked to use of fossil fuels. Microorganisms play pivotal roles in converting macroalgae into valuable products, and metabolic engineering technologies have been developed to extend their native capabilities. This review showcases current achievements in engineering the metabolisms of various microbial chassis to convert red, green, and brown macroalgae into bioproducts. Unique features of macroalgae, such as seasonal variation in carbohydrate content and salinity, provide the next challenges to advancing macroalgae-based biorefineries. Three emerging engineering strategies are discussed here: (1) designing dynamic control of metabolic pathways, (2) engineering strains of halophilic (salt-tolerant) microbes, and (3) developing microbial consortia for conversion. This review illuminates opportunities for future research communities by elucidating current approaches to engineering microbes so they can become cell factories for the utilization of macroalgae feedstocks.

Fei Wang, Dawei Duan, Mriganka Singh et al.

Over the past decade, perovskite photovoltaics have approached other currently available technologies and proven to be the most prospective type of solar cells. Although the many‐sided research in this very active field has generated consistent results with regard to their undisputed consistently increasing power conversion efficiency, it also produced several rather contradictory opinions. Among other important details, debate surrounding their proneness to surface degradation and poor mechanical robustness, as well as the environmental footprint of this materials class, remains a moot point. The application of ionic liquids appears as one of the potential remedies to some of these challenges due to their high conductivity, the opportunities for chemical “tuning” of the structure, and relatively lower environmental footprint. This article provides an overview, classification, and applications of ionic liquids in perovskite solar cells. We summarize the use and role of ionic liquids as versatile additives, solvents, and modifiers in perovskite precursor solution, in charge transport layer, and in interfacial and stability engineering. Finally, challenges and the future prospects for the design and/or selection of ionic liquids with a specific profile that meets the requirements for next‐generation highly efficient and stable perovskite solar cells are proposed.

Mohsen Esamaeilpour, Majid Ghahraman Afshar, Morteza Faghihi et al.

Lack of water is considered the most important threat to the survival of human beings and natural ecosystems. Food and energy security, health and industrial progress, which are the main components of the sustainable development of societies, depend on water more than anything else. Water consumption in Loshan power plant is 2600 L/MWh raw water, which is very high compared to the production capacity and is due to having a tower in its cooling system. Considering the high volume of water consumed in this power plant compared to the production efficiency and on the other hand, reducing the amount of rainfall and droughts that have occurred, it is necessary to provide a solution to modify the consumption pattern for this power plant. Considering the special position of this power plant, solutions to modify the water consumption pattern by recycling clean drains (blow down of boilers, back wash of sand filters and water sampling), purification and recycling the blow down of cooling towers into the water cycle and optimization of cooling towers (replacing drippers, replacing nozzles and optimizing water distribution to increase efficiency, using a rotating nozzle to increase the time and speed of the water droplets hitting the air in order to transfer more heat) are suggested. According to the experimental data, the conductivity of boiler blowdown is about 10 μs/cm and the expenses of recycling the blowdown of boiler by using heat exchanger is estimated about 324.425.000 Rials.

Zhiwei Li, Feng Lu, Mengmeng Liu et al.

Abstract The association between CO and chronic obstructive pulmonary disease (COPD) has been widely reported; however, the association among patients with type 2 diabetes mellitus (T2DM) or hypertension has remained largely unknown in China. Over‐dispersed generalized additive model was adopted to quantity the associations between CO and COPD with T2DM or hypertension. Based on principal diagnosis, COPD cases were identified according to the International Classification of Diseases (J44), and a history of T2DM and hypertension was coded as E12 and I10‐15, O10‐15, P29, respectively. A total of 459,258 COPD cases were recorded from 2014 to 2019. Each interquartile range uptick in CO at lag 03 corresponded to 0.21% (95%CI: 0.08%–0.34%), 0.39% (95%CI: 0.13%–0.65%), 0.29% (95%CI: 0.13%–0.45%) and 0.27% (95%CI: 0.12%–0.43%) increment in admissions for COPD, COPD with T2DM, COPD with hypertension and COPD with both T2DM and hypertension, respectively. The effects of CO on COPD with T2DM (Z = 0.77, P = 0.444), COPD with hypertension (Z = 0.19, P = 0.234) and COPD with T2DM and hypertension (Z = 0.61, P = 0.543) were insignificantly higher than that on COPD. Stratification analysis showed that females were more vulnerable than males except for T2DM group (COPD: Z = 3.49, P < 0.001; COPD with T2DM: Z = 0.176, P = 0.079; COPD with hypertension: Z = 2.48, P = 0.013; COPD with both T2DM and hypertension: Z = 2.44, P = 0.014); No statistically significant difference could be found between age groups (COPD: Z = 1.63, P = 0.104; COPD with T2DM: Z = 0.23, P = 0.821; COPD with hypertension: Z = 0.53, P = 0.595; COPD with both T2DM and hypertension: Z = 0.71, P = 0.476); Higher effects appeared in cold seasons than warm seasons on COPD (Z = 0.320, P < 0.001). This study demonstrated an increased risk of COPD with comorbidities related to CO exposure in Beijing. We further provided important information on lag patterns, susceptible subgroups, and sensitive seasons, as well as the characteristics of the exposure‐response curves.

Chiharu Nishita‐Hara, Hiroshi Kobayashi, Keiichiro Hara et al.

Abstract Oxidative stress is a mechanism that might raise the toxicity of mineral dust aerosols. We evaluated the oxidative potential (OP) of four reference materials (RMs) of mineral dusts using dithiothreitol assay. The OP of the water‐soluble fraction of the dust RMs accounts for 40%–70% of the OP of the total fraction. The values of total and water‐soluble OP normalized by the surface area of insoluble particles showed agreement among the different dust RMs. The surface area of insoluble dust particles was therefore inferred as an important factor affecting the OP of mineral dust. Using the relation between total OP and the surface area of insoluble particles of the dust RMs, we estimated the total OPs of fine and coarse atmospheric mineral dust aerosols assuming a typical particle size distribution of Asian dust aerosols observed in Japan. Mass‐normalized total OPs were estimated at 44 and 23 pmol min−1 μg−1 for fine and coarse atmospheric mineral dust particles. They closely approximate the values observed for urban aerosols in Japan, which suggests that mineral dust plume advection can lead to a marked increase in human exposure to redox‐active aerosols, even far downwind from mineral dust source regions.