International Journal of Environmental Science and Technology最新文献

The accurate prediction of significant wave height is essential for coastal and offshore engineering, and is especially important for producing renewable ocean wave energy. However, H_s is traditionally predicted using empirical or numerical models, which lack accuracy, are computationally demanding, or require extensive datasets. Due to chaotic nature, it is very challenging for empirical or numerical models to precisely predict H_s. This study developed and tested several standalone machine learning models for H_s prediction and explored hybrid versions of these models based on additive regression to further improve model accuracy. Half-hourly H_s data along with common variables measured at ocean buoys were collected from four sations (i.e., Mooloolaba, Gladstone, Caloundra and Brisbane) along the coastline of Queensland, Australia and used to develop the ML models. The ML models were tested for their ability to accurately predict H_s at Mooloolaba station and were transferred to the three other stations to prove their spatial generalization capabilities. Overall, the results demonstrate that the ML models, and especially their hybrid versions, can accurately predict H_s at Mooloolaba as well as for other stations. Thus, the proposed models may serve as promising tools for improving ocean wave energy production.

To develop and build advanced marine systems in underwater environments, it is essential to thoroughly analyze how sound waves travel and how oceanic physical phenomena impact sound propagation. One such phenomenon, called mesoscale eddies, can be found in various bodies of water like the Persian Gulf and the Sea of Oman. These eddies are particularly noticeable during the summer months. To study sound propagation in mesoscale eddies, this research utilized a Range-dependent Acoustic Model (RAM). This model provides an accurate solution for marine acoustic problems when given the correct inputs. The aim of this article is to identify the sound propagation patterns in different areas of the Persian Gulf and the Oman Sea to assist engineers in various applications. In other words, the results of this article help designers locate their sound sources and receivers based on the acoustic pressure profile and sound transmission loss in areas with different depths in the Persian Gulf and the Oman Sea. The findings revealed that the acoustic pressure is lowest in areas where a salty core eddy exists. This means that sound cannot penetrate areas with high density at the center of these eddies. Instead, the sound is redirected towards areas with the slowest speed. Furthermore, the acoustic RAM output shows a reversal in the acoustic pressure profile and transmission loss profile. As a result, the results of the acoustic model indicate that the sound did not effectively penetrate the eddy center or other deep areas.

The removal of nitrogen in wastewater treatment systems is temperature-sensitive, with lower temperatures inhibiting the activity of nitrogen-removing bacteria. To mitigate this during cold seasons, a combined approach of bioaugmentation and mud-film symbiosis technology was applied to domestic wastewater secondary effluent. Biological agents A (nitrifying bacteria) and B (denitrifying bacteria) were introduced at 6–8 °C, with a 7-day incremental dosing regimen. The outcomes revealed significant enhancements in Total Nitrogen, NH_3–N, NO₃–N removal rates, and simultaneous nitrification–denitrification (SND) efficiency by 30.73%, 37.55%, 32.25%, and 43.69%, respectively, compared to untreated low-temperature conditions.

High-throughput sequencing analysis demonstrated an increased abundance of nitrifying and denitrifying microbial communities, including Nitromonas, Nitrobacterium, Truepera, Dechloromonas, and Unclassified Aeromycetes, in the floating biofilm and activated sludge. This augmentation of nitrogen removal capacity underscores the importance of bioaugmentation in strengthening the SND process, ensuring effective nitrogen removal in cold winter conditions for wastewater treatment systems. The findings provide valuable insights into enhancing nitrogen removal efficiency in wastewater treatment during cold periods.

This review deals with the applicability of polymeric sorbents in removing spilled free oil from water surfaces. The theoretical framework covers the sorption ability of polymeric materials in general, respecting their size and morphology; however, the main focus is on polyolefins, primarily various grades of polyethylene (PE) and polypropylene (PP), including PE and PP waste. The core motivation associated with recycling polyethylene LDPE is the low interest in plastic convertors and the limited marketability of these commodities. The scientific focus in this area is on the development of new products having at least two general features: i) a specific application that does not require high mechanical performance, and ii) the material has a unique functionality that is not significantly influenced by using a recyclate against the use of the pristine polymer. Recycled polyolefins fully satisfied these requirements. This review pays special attention to the theoretical aspects of polymeric sorbents. Specific features of sorbents are analyzed depending on their geometry and morphology, involving powders, membranes/mats, and 3D foams (sponges)/gels. The wettability and sorption mechanisms regarding the chemical composition of materials, their surface topology, and internal porosity are discussed in detail. The presented manuscript emphasizes the close connection between materials’ behavior and properties, which is crucial for efficient oil/water separation and the theoretical modeling of adsorption and absorption processes. The focus on the physical aspects of materials from a theoretical point of view is highlighted, enabling a complex understanding of the oil/water separation processes.

This study aimed to characterize and study the toxic potential of microparticles generated from kitchen sponges (flexible polyurethane foam and abrasive synthetic fiber) and compare them with microparticles from loofah (Luffa cylindrica) for microcrustaceans Daphnia magna and Artemia salina. In addition, the toxicity of the leachate, flexible polyurethane foam particles, and abrasive fiber was evaluated. The structural and morphological characterization of the samples was carried out through the analysis of spectroscopy in the infrared region with Fourier Transform, scanning electron microscopy, and X-ray diffraction. To determine the diameter of the particles, the technique of sieving granulometry. Microcrustaceans were exposed to microparticle concentrations ranging from 20 to 200 mg L⁻¹ for Daphnia magna and 1 to 23 g L⁻¹ for Artemia salina. The microparticles and the leachates from the multipurpose sponge showed acute toxicity for the microcrustaceans, determined through the average effective concentration (EC_50(48 h)) of the sponge microparticles and the toxicity factor (TF_48h) of the leachates. This work addresses the characterization of the materials that make up the multipurpose sponge and the vegetable loofah and brings evidence of the toxicity of microparticles and leachate generated by these materials. These findings suggest that organism size and material composition significantly influence microplastic toxicity. Loofah emerges as a more environmentally friendly alternative to synthetic sponges, since it does not show acute toxicity and is biodegradable.

Graphical Abstract

Combating climate change is one of the key topics and concerns that our community is currently facing these days. Since a few decades ago, greenhouse gases emissions gradually started to increase. Thus, the researchers attempted to find a permanent solution for this challenge. In this paper, different methods of machine learning and deep learning models are applied to evaluate their effectiveness and accuracy in predicting greenhouse gases emissions. To increase the accuracy of the assessment, the data of 101 countries over a period of 31 years (1991–2021) from the official World Bank sources are considered. In this study, therefore, a range of matrices are analyzed including Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, p value, and correlation coefficient for each model. The results demonstrate that machine learning models typically overtake the deep learning models with the support vector regression polynomial model. Besides, the statistical findings of longitudinal regression analysis reveal that by increasing cereal yield, and permanent cropland areas the greenhouse gas emissions are significantly increase (p value = 0.000) and (p value = 0.06) respectively; however, increasing in renewable energy consumption and forest areas will lead to decreasing in greenhouse gas emissions (p value = 0.000) and (p value = 0.07) respectively.

Effective utilization of data analysis techniques is paramount in addressing the complex challenges presented by environmental issues. These methodologies empower researchers and practitioners to derive meaningful insights from intricate datasets encompassing air quality, biodiversity, climate change, and other pivotal environmental factors. Through the deployment of robust classification models, such as intelligent classifiers, researchers can accurately classify and predict environmental phenomena. This capability holds significant implications for guiding policy decisions, mitigating environmental risks, and devising sustainable solutions to protect our natural resources and ecosystems. Thus, classification models not only deepen our comprehension of environmental dynamics but also empower proactive measures towards achieving environmental sustainability and resilience amidst global challenges. Intelligent classifiers, distinguished by their exceptional capabilities, have demonstrated superior performance compared to other classification models. However, in all developed intelligent classifiers a similar cost/loss function is implemented in the learning processes, which is continuous and works based on the distance between actual and fitted values. Whereas the nature of the classification is discrete. As a result, in this study, a novel cost/loss function is proposed that in contrast to its conventional version is discrete and works based on the direction. In order to explain the process of the suggested methodology, the feed-forward multilayer perceptrons that are among the most famous intelligent classifiers is considered. In this paper, in order to determine the superiority of the proposed model in the domain of environment, it is implemented on some benchmark data sets which is related to air quality. Numerical results indicate that the performance of the proposed model is better than the conventional multilayer perceptrons in whole benchmark data sets. In addition, numerical results clarify that the developed discrete learning-based multilayer perceptron classifier can averagely gain an 87.68% classification rate, which points to more than 9% improvement over its conventional version.

The increase in the world’s population is producing waste proportionately, which plays a crucial role in air, water, and soil pollution and contamination. Therefore, the present study focuses on valorizing agro-waste by extracting phytochemicals and determining phenolic content, total flavonoid, and total antioxidant capacity using gallic acid, rutin, and ascorbic acid as standard. Further, pectin extraction from agro-wastes by traditional and microwave-assisted methods was compared. Of the various agro-wastes studied, pomegranate peel and maple leaf illustrated higher flavonoid (314.25 ± 3.30 and 350.26 ± 3.48 mg rutin equivalent/100 g in methanol extract), total phenolic content (48.36 ± 2.33 and 47.96 ± 1.67 mg gallic acid equivalent/100 g in methanol extract), and total antioxidant capacity (55.03 ± 2.56 and 50.45 ± 1.02 µg ascorbic acid equivalent/g dry weight in aqueous extract). Different solvents used in extraction showed distinct potentials for evaluating total phenolic content, total flavonoid, and antioxidant capacity. Also, the antibacterial potency of the aqueous extract of pomegranate peel exhibited the highest inhibition zone against Cellulomonas sp. (S-10) and Bacillus sp. (S-17) among the locally isolated pectinase-producing bacteria. At the same time, pumpkin pulp + seeds did not show any inhibition. Besides, the study revealed higher pectin yield from pumpkin pulp + seeds followed by orange peel, banana peel, pomegranate peel, and others.

This study supports different agro-wastes as potential low-cost resources for the sustainable production of phytochemicals. In addition, those agro-wastes exhibited antibacterial potency and can be used in the pharmaceutical industries. Therefore, this study aims to decrease agricultural waste by utilizing them in producing value-added products, which ultimately helps sustainable economic development and pollution control.

Solid waste management has been one of the challenging tasks for the waste collectors at Dhaka North City corporations, especially during the post-COVID-19 period, since this task exposes several acute and chronic illnesses. The perilous implications of waste collection on the health condition of the waste collectors at the Dhaka North City Corporation (DNCC) have often been ignored, and they have to work without adequate protective measures. Though different research has already been done regarding the occupational health hazards of waste collectors, the novelty of this study is that it considers the post-COVID period and focuses on the solid waste picker of DNCC. This study aimed to evaluate the current health hazards faced by DNCC household waste collectors and suggest remedial actions. A questionnaire-based survey (n = 415) assessed work environment, socio-economic status, and post-COVID-19 health hazard awareness through convenience sampling. Descriptive statistics, Pearson’s chi-square tests, and binary logistic regression were adopted to analyze the data. Descriptive statistics portray that 81.8% of waste pickers are not satisfied with their work environment. Additionally, 66% of solid waste collectors face health issues, and 58% of waste collectors are unaware of the risk of COVID-19. Pearson’s chi-square tests reveal that the health hazards of waste pickers are significantly associated with job type, working hours, Awareness of solid waste effect on health and severe suffering history. Additionally, the binary logistic regression model exposed job type, Awareness of solid waste effect on health, frequently suffered diseases, and severe suffering history has significant (p-value < 0.05) impact on the health hazard of a waste picker of DNCC. Regarding the policy implication, Dhaka North City Corporation must take immediate action that will significantly reduce the hazardous impacts of solid waste collection on the health of the waste collectors by supplying them with adequate protective measures.