Snežana Filip

Air pollution data processing is a relevant aspect of urban-life-quality monitoring. This study relates the values of air pollutants to meteorological factors with prediction models created with multiple linear regression (MLR) in the R program. Fragmented data were obtained in the years 2023/24 from the heaviest-urban-traffic location in Belgrade, Serbia. A Serbian-comparable list of air pollutants (PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃) was created according to an analysis of sensor availability at monitoring stations. With the prediction models and seasonally clustered data (spring and summer data clusters were incomplete), each of these pollutants (except O₃) was related to all measurable meteorological factors (atmospheric pressure, wind speed, relative humidity, and temperature). All independent (meteorological) factors showed a moderate level (Multiple R² < 0.7) of impact on the prediction of air pollutant values (dependent factors). The second group of prediction models related pollutant triplets, and fourteen of the created models reached R² > 0.7. The prediction model with the highest relevant R² value (CO~PM₁₀ + NO₂) was used for CO pollutant prediction and in-sample fit analysis. The obtained mean absolute error is ~0.13 mg/m³. This case study contributes to the examination of the role of meteorological factors in urban-traffic air-quality monitoring within a seasonal context.

The application of CO₂ for enhanced oil recovery requires reliable operation of the injection system and the integrity of its technical and geological components. The aim of this study is to systematically identify potential causes and failure events that may lead to the loss of safe and effective CO₂ injection in a CO₂-EOR system. Based on a review of relevant scientific literature and the defined system boundaries, a qualitative Fault Tree Analysis (FTA) model was developed. The top event was divided into two main groups of events: loss of effective injection capability and loss of safe CO₂ containment during injection. The developed model includes surface, wellbore, reservoir, and geomechanical failure pathways, including CO₂ supply and compression problems, corrosion, loss of injectivity, loss of well barrier integrity, and unfavorable reservoir pressure response. The results show that FTA enables different failure mechanisms to be integrated into a structured cause-and-effect model and provides a basis for their further quantitative assessment and prioritization.

The development of plant-based synbiotic beverages is gaining increasing attention as consumers seek sustainable, functional alternatives to dairy products. This preliminary study investigated the fortification of tibicos (water kefir) with lemon catnip (Nepeta cataria var. citriodora) hydrolate, an aromatic distillation byproduct rich in bioactive terpenoids. After 72 h-fermentation of tibicos, physicochemical, microbiological, health-promoting and sensory parameters were evaluated. Both control and fortified beverages exhibited typical fermentation kinetics, including a decrease in pH, reduction of soluble solids, and accumulation of organic acids. Lactic acid bacteria count remained stable, while yeast proliferation was slightly reduced in the hydrolate-fortified sample, consistent with the known yeast-sensitive nature of certain hydrolate-derived terpenoids. Importantly, hydrolate fortification significantly enhanced antioxidant capacity (DPPH: +34%; ABTS: +39%; RP: +38%). Enzyme-inhibitory activities also increased significantly in the hydrolate-fortified samples (α-Amylase and α-Glucosidase inhibition rates increased by 9% and 11%, respectively). ACE inhibition similarly increased from 32% to 44%, indicating an enhanced antihypertensive potential. HMG-CoA reductase inhibition increased from 31% to 42%, showing improved hypolipidemic activity. Sensory evaluation indicated improved sensory acceptability, imparting citrus–floral notes that balanced the acidic profile of tibicos. These findings highlight the potential of valorizing lemon catnip hydrolate as a functional fortifier in non-dairy synbiotic beverages.

The fruit of Maclura pomifera is the subject of scientific research due to the presence of different classes of flavonoid compounds. Two of them, osajin and pomiferin, are the most studied. The fruit is also a good source of triglycerides and triterpenes, which classifies Maclura as a raw material for obtaining extracts with possible use in the cosmetic and pharmaceutical industries. The paper gives a comparative overview of the chemical profile of Maclura and shea butter, which are precious in cosmeceuticals.

Bioplastics are kind of plastics produce from natural and renewable raw materials biomass sources such as sugarcane, corn starch, wood, waste paper, vegetable oils and fats, bacteria, algae, etc. As an alternative to conventional petrochemical plastics, bioplastics are naturally degraded in the environment, and are not harmful to nature environment because it can decompose back into carbon dioxide. Thus, the products made from bioplastics are renewable, biodegradable, compostable and environmentally friendly. The aims of this short review are to present classifications of bioplastic, their advantages and disadvantages, processing, applications and the process of biodegradation.

Polyphenolic compounds are of great interest in today’s science. Naturally, they occur in plants and other sources in many different forms. Their wide range of biological activity has attracted the attention of the scientific community. One of the sources of phenolic compounds is stinging nettle (Urtica dioica L.), a common plant in almost all parts of the world. A long tradition of utilization and an interesting chemical profile make this plant a fascinating and extensive object of study. The chemical profile also allows this plant to be used as a food and a pigment source in the food, pharmaceutical, and cosmetic industries. Previously conducted studies found phenolic acids and polyphenolic compounds in root, stalk, and stinging nettle leaves. Different extraction techniques were usually used to isolate them from the leaves. Obtained extracts were used to investigate biological activity further or formulate different functional food products. This study aimed to collect all available knowledge about this plant, its chemical composition, and biological activity and to summarize this knowledge with particular attention to polyphenolic compounds and the activity and mechanisms of their actions.

In this paper we are studying the problems of oil manufasturing and methods for solving them on the oil field in Amal. Problems on the oil field in Amal are: deposition of paraffin, occurrence of stratified sand, inefficient operation of the gas-lift system, salt deposition, inefficient oil desalination, and environmental problems. Without taking additional measures and activities, production problems may worsen, causing an even greater reduction in oil production.

Air pollution is one of the most challenging and complex problems of our time. This research presents the prediction of air pollutant values based on using an R program with linear regression. The research sample consists of obtained values of air pollutants such as sulphur dioxide (SO₂), particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrite oxides (NO, NO₂, and NO_X), atmospheric data pressure (p), temperature (T), and relative humidity (rh). The research data were collected from the city of Belgrade air quality monitoring reports, published by the Environmental Protection Agency of the Republic of Serbia. The report data were transformed into a form suitable for processing by the R program and used to derive prediction functions based on linear regression upon pairs of air pollutants. In this paper, we describe the R program that was created to enable the correlation of air pollutants with linear regression, which results in functions that are used for the prediction of pollutant values. The correlation of pollutants is presented graphically with diagrams created within the R GUI environment. The predicted data were categorized according to air pollution standard ranges. It has been shown that the derived functions from linear regression enable predictions that are well correlated with the data obtained by automatic acquisition from air quality monitoring stations. The R program was created by using R language statements without any additional packages, and, therefore, it is suitable for multiple uses in a diversity of application domains with minor adjustments to appropriate data sets.

Rosemary (Rosmarinus officinalis L.) is a plant worldwide cultivated mainly for essential oils, extracts, and as a spice. Up-to-date results showed diversity in composition of the essential oils, which may influence their quality, biological activity, and thermal properties. Therefore, the aim of this study was to investigate the chemical composition, antimicrobial activity, and thermal properties of the rosemary essential oils originating from Serbia and Russia. Additionally, oils were added to the sunflower oils in order to investigate possible antioxidant activity during the frying. Investigation of the chemical profile marked α-pinene, eucalyptol, and camphor as the most abundant compounds in both oils. However, overall composition influenced in such manner that Russian oil showed significantly higher antimicrobial activity, while Serbian oil proved to be better antioxidant agent in case of frying of sunflower oil. This would significantly influence possible application of the oils, which could be used as an antioxidant agent for extension of the food shelf life, or antimicrobial agent for protection against different microbial strains.