In this research, we present the development and validation of a compact, resource-efficient (low-cost, low-energy), distributed, real-time traffic and air quality monitoring system. Deployed since November 2023 in a small town that relies on burning various fuels and waste for winter heating, the system comprises three IoT units that integrate image processing and environmental sensing for sustainable urban and environmental management. Each unit uses an embedded camera and sensors to process live data locally, which are then transmitted to a central database. The image processing algorithm counts vehicles by type with over 95% daylight accuracy, while air quality sensors measure pollutants including particulate matter (PM), equivalent carbon dioxide (eCO₂), and total volatile organic compounds (TVOCs). Data analysis revealed fluctuations in pollutant concentrations across monitored areas, correlating with traffic variations and enabling the identification of pollution sources and their relative impacts. Recorded PM10 daily average levels even reached eight times above the safe 24 h limits in winter, when traffic values were low, indicating a strong link to household heating. This work provides a scalable, cost-effective approach to traffic and air quality monitoring, offering actionable insights for urban planning and sustainable development.

Humanity is a fossil-fueled civilization with a large influence on the environment. The World Health Organization (WHO) has pointed out that air pollution is now the single biggest environmental threat to human health. The air quality in Petroșani, a traditional mining region from the Jiu Valley bituminous coal basin, Romania, is rarely debated; however, it is not often investigated. In this paper, the main air pollution sources of Petroșani are identified and the performed measurements emphasize the air quality in the area of its transit road. The monitoring program set out the objectives, parameters, and points of the monitoring system, as well as the frequency and duration of the program and other monitoring parameters. The equipment used was provided by the National Institute for Research and Development in Mine Safety and Protection to Explosion from Petroșani, within an institutional partnership with the University of Petroșani. The monitoring of the air quality parameters was conducted from March to July 2020, at six points located on the road that crosses the city. It was thus possible to capture a variety of concentrations of the monitored parameters in different weather conditions to determine the air quality in this area. Based on the variation of the measured values in one of the most important historical Romanian bituminous coal mining basins, the preliminary results suggest a worsening of local air quality parameters in relation to the decarbonization process.

Air quality is an essential factor for human health and ecosystem balance, but in regions like Petroșani Mountain Depression, air pollution continues to be a significant challenge. This area, marked by decades of coal mining, is confronted with high concentrations of pollutants, influenced by human activities and the specific geography and climate. This study aims to compare instrumental air quality measurements with snow sample analysis, as a sustainable alternative method. Specifically, it examines the spatiotemporal distribution and evolution of air pollutants, utilizing long-term monitoring data and an extensive sampling network (42 points) for both air and snow, to provide a thorough understanding of air quality dynamics in the area. The experimental part of this study focused on determining VOCs and PM in the air, and dissolved ions (sulfate, calcium, and magnesium) and suspended solids in snow. The results highlight significant correlations between pollution sources and atmospheric dynamics in mountain depressions, while also analyzing the efficiency of the instruments used for data collection. This study emphasizes that, although instrumental methods provide precise and detailed measurements, their implementation in isolated regions presents significant challenges. Therefore, alternative approaches such as snow analysis can represent a more efficient and sustainable option in these regions.

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions.

Monitoring vegetation biomass dynamics is essential for assessing ecosystem functioning and biodiversity pressures in protected mountain areas, where reduced accessibility limits in situ data collection. This study investigates the multitemporal variation in vegetation biomass within the Cioclovina–Șura Mare–Piatra Roșie strictly protected area of the Grădiștea Muncelului–Cioclovina Natural Park (Southern Carpathians, Romania), using vegetation indices derived from Sentinel-2 imagery for the 2018–2022 period. Four complementary indices (NDVI, SAVI, MSAVI, and LAI) were computed and normalized, then integrated into an original synthetic indicator (BCIS—Biomass Change Integrated Score) for quantifying biomass changes. The results indicate an overall reduction in vegetation biomass, with 89.49% of the area classified under degradation trends, while 4.53% shows regeneration processes. Grasslands and mixed agricultural–natural lands are the most affected habitats, where degradation is linked to anthropogenic pressures and ecotonal vulnerability, whereas broadleaf forests display a high degree of resilience, maintaining substantial proportions of stable or regenerating surfaces. The multispectral integration through the BCIS indicator enabled a more robust detection of critical zones, supporting sustainable vegetation management and biodiversity monitoring in protected mountain ecosystems.

Urban forests serve as representations of nature within city landscapes. Green Forest, spanning 5,198,412 square meters, has been incorporated into the Municipality of Timișoara’s public domain and designated as a forest park. This fact increased green space per capita and enriched biodiversity within Timișoara’s landscape architecture. This study explores the diversity of Green Forest trees and highlights their contribution to urban landscapes. Statistical methods, including comparative and linear relationships analyses, were employed to assess significant variations in the dendrometric parameters of the analyzed tree species: mean tree height, mean diameter at breast height (DBH), tree age, and stand density. Principal Component Analysis (PCA) and cluster analysis were applied to uncover underlying patterns in the data. Using ArchiCAD and Lumion, high-quality 3D visual representations were developed for an ecological education area, an active recreation region, and a passive recreation area within Green Forest. Due to their morphological characteristics and phenotypic traits, the predominant tree species include Quercus robur, Quercus cerris, Quercus rubra, Fraxinus excelsior, Acer platanoides, Acer pseudoplatanus, Ulmus campestris and Robinia pseudoacacia, contribute to Timișoara’s urban aesthetic. Moreover, the results of the dendrometric analysis provide a foundation for further research in urban ecology. A key practical application of this study is landscape design renderings, which provide detailed and realistic visualizations to effectively communicate the design and functionality of Green Forest’s spaces. If implemented, these developments will encourage public engagement with nature, promoting mental and physical well-being within the community.