This paper presents the investigation of the air-pollution management possibility in Bor (Serbia) using statistical analyses of the correlation dependence of SO₂ pollution concentration and meteorological parameters. This kind of approach allows data to determine the shape of time trends; models serial correlation and dependence on covariance; and provides statistical analyses of data. Air quality control in the area of Bor is measured at four monitoring stations: City park; Institute of Mining and Metallurgy; Jugopetrol oil logistic centre and Brezonik suburb. There is also a meteorological station near the aforementioned institute that measures temperature; air humidity; atmosphere pressure; wind speed and direction. The results received from all monitoring stations are processed by the Institute of Mining and Metallurgy in Bor. The data from the reported documents were used for statistical analyses, for determining the correlation between SO₂ emission concentration and meteorological parameters. The source of SO₂ emissions is the local copper smelter, built within the town of Bor from 1961–1963. However, the disposition of the pollutant concentration is influenced by meteorological parameters (air humidity, atmosphere pressure, speed and wind direction). The successful application of these statistical analyses in this paper points to a possibility of its usage in air pollution studies.

Noise generated by air traffic is one of the leading problems in urban areas in the world. That is why the measures for reducing the noise generated by air traffic are today the subject of constant improvement, extensive research, and standardization. Monitoring and predicting air traffic noise levels represent an important factor in quality control and noise management. In order to reduce the noise level in the vicinity of Mitiga International Airport (Tripoli) in Libya, the aim of this paper is to assess the noise emission levels before and after the introduction of certain measures to reduce airborne noise, as well as to predict the noise level at the analyzed area. The method of interpolation in the Geographic Information System (GIS) was used to graphically represent the spatial distribution of noise emissions in the study area, and by applying zonal statistics deviations from the allowable values for each control grid were calculated. The obtained results showed that the application of certain measures has a great impact on reducing noise levels on controlled grids. Finally, the reliability of noise level prediction was successfully assessed using the artificial neural network (ANN) method.

This study elucidates the findings derived from the implementation of the integrated AHP-PROMETHEE multi-criteria decision-making method aimed at selecting the most suitable electric vehicle for urban transport. The AHP methodology facilitated the identification of critical criteria influencing the selection of an electric vehicle for urban use, including factors such as price, battery warranty, charging speed, acceleration to 100 km/h, maximum speed, engine power, efficiency, battery performance, and mileage. The subsequent application of the PROMETHEE method allowed for the ranking of nine electric vehicles based on these established criteria. Each vehicle alternative was assessed regarding its capacity to fulfill the specified requirements and preferences of the decision-makers. The analysis revealed that the Mini Cooper E emerged as the model that most effectively aligns with the prioritized criteria. This vehicle is deemed the optimal selection for urban transport needs, considering all pertinent factors. The decision to select a specific electric vehicle is expected to influence the efficiency, sustainability, and economic viability of the urban transportation system. Furthermore, it is advisable to maintain ongoing surveillance of advancements in electric vehicle technology and charging infrastructure to ensure that the selected model continues to represent the best option in the future.

The growing trend of the urban population looks for the formation of the concept of Smart Cities, based on the principles of sustainable development, which will meet the needs of people who live in them. The city must develop its public service infrastructure to provide all the requirements for the population. One of the requirements, people living in cities is the security of public areas managed by the city. The system of video surveillance, public areas should contribute to the personal safety of citizens, children and traffic, reducing the rate of crime and more efficient operation of public services. This research aims to examine academics' opinions (perceptions) about the impact of security cameras on increasing security within the concept of Smart Cities. The research had conducted on the territory of Serbia through an online survey. The obtained data were processed statistically by Chi-square and T-test, ANOVA method, multiple regression and correlation using SPSS application. The research indicates that academics much more believe in the contribution of video surveillance, reducing the crime rate, than studies of the effectiveness of the use of video surveillance shows. Video camera monitoring accelerates the reactions of competent services, but video camera recordings have not always been accepted as evidence in court.