This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine-learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from Internet of Things (IoT) sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R² score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO₂, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air-quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

With the intensification of traffic in large urban agglomerations, the need for monitoring of environmental parameters is increasingly being observed and more and more efficient solutions are being sought for taking over and processing these data. It is proposed to build a network of sensors mounted on electric vehicles to be used to create an interactive map of environmental pollution in urban areas. It has often been observed that motorized air quality monitoring platforms are propelled by conventional gasoline or diesel engines and therefore also produce significant pollution factors that can alter data taken over by the sensor network. The use of an electrical monitoring system has as a major benefit greater accuracy in retrieving data from pollution detectors as it does not appear in the data taken and the influence of classic monitoring system pollution using a classic propulsion platform. It is also proposed that efficient communication algorithms be made between the sensor networks to take data on the quality of the environment.

The Internet of Things (IoT) has developed tremendously over the past few years and has proven its worth in many areas of activity. With regard to environmental air quality monitoring, there are more and more products and applications that try to gather as much data as possible about all the pollution factors in a given area. This paper aims to present a new method of using devices capable of communicating with each other using the LoRa communication protocol to report to a real-time central server on environmental air quality. The innovation of this paper is the fact that it is implemented using a developed LoRa localization protocol, which connects an air quality sensor network, using only the low power of the LoRa technology by applying a multilayer algorithm to the gateway timestamps from received packages. The so created LoRaWAN tracking system is able to exploit transmitted packets to calculate the current position without using GPS or GSM that are high power consumers.

The success of the development of electric vehicles and the increase in demand for their purchase amid greater autonomy of power systems and the development of charging infrastructure also highlights the possibility of using these vehicles as an integral part of a Smart Grid system in the context of sustainable urban development around the extended ideas of the Smart City concept in which other innovative concepts can be integrated. This paper aims to present new research developed around the idea of Smart City in Petroşani, starting from electric vehicles that can serve both as a means of transport and monitoring system of environmental parameters but can also be integrated into the electricity network so that in periods of high consumption it can supply electricity when needed and in periods of over-production to charge their batteries. Although at present at the level of Petroşani there are not many electric vehicles that can become part of the electricity network, this idea is applicable especially in a public or private facility with the possibility of scaling in the future.

In this paper, we investigate the impact of Smart Parking Systems (SPS) on urban mobility and the incorporation of technological innovations within the electric vehicle (EV) industry, focusing on Petrosani, Romania. As urban areas grapple with the challenges of rapid development, such as traffic congestion and inefficient use of parking spaces, SPS emerge as a crucial solution. This research presents a comprehensive analysis of Petrosani's current traffic and parking conditions, leveraging public opinion surveys and traffic flow studies to propose an efficient integration of smart parking systems. The study reveals significant public support for SPS, highlighting a community eager to embrace solutions that enhance urban mobility, reduce environmental pollution, and improve the quality of urban life. Our findings suggest that adopting SPS in Petrosani could lead to more effective traffic management, better utilization of parking resources, and a shift towards sustainable urban development. The paper underscores the need for strategic planning and community involvement in transitioning towards smarter, more sustainable urban ecosystems, setting a precedent for similar initiatives in other cities. This work contributes to the discourse on sustainable urban planning, emphasizing the practical implications and benefits of intelligent parking solutions in enhancing urban mobility and environmental sustainability.