The implications of the COVID-19 outbreak are subjected to an increasing number of studies. So far, air quality trends related to the lockdown due to the pandemic have been analysed in large cities or entire regions. In this work, the region studied is the metropolitan area of Cagliari, which is the main city on the island of Sardinia (Italy) and can be representative of a coastal city that includes industrial settlements. The purpose of the study is to evaluate the effect of restrictions related to the COVID-19 outbreak on air quality levels and the traffic dynamics in this type of urban area. Nitrogen Dioxide (NO₂) levels before, during and after COVID-19 lockdown have been investigated using data acquired from the Sentinel-5P/TROPOMI satellite combined with on-site measurements. Both TROPOMI detected and ground-based data have revealed higher levels of NO₂ before and after the lockdown, compared to those during the period of COVID-related restrictions, in particular in the urban area of Cagliari. On the other hand, NO₂ registered in the oil refinery area did not show significant differences associated with lockdown. The correlation of TROPOMI NO₂ tropospheric column with ground data (surface NO₂) on a monthly mean basis showed different values based on the background and the highest Pearson's coefficient was of about 0.78 near to the city centre, where traffic can be considered a significant source of emission. In addition, a comparison of the air pollution level with the dynamics of vehicle traffic was investigated. The study highlighted a remarkable correlation between the reduction of the number of vehicles and the corresponding tropospheric NO₂ values that decreased on a weekly mean basis.

Important objectives of the four-year enviroGRIDS project encompass the improvement of transnational cooperation, the use of state of the art Information and Communication Technologies for data analysis and sharing and the application of environmental models for monitoring present and predicting future states of the environment for the Black Sea region. In such a transnational context, there is a dire need for the environmental sciences to evolve from a simple, local-scale vision toward a complex, multi-user, multilayered holistic approach. BASHYT (http://swat.crs4.it/) is a Web based, GIS oriented, information and support tool, part of the Black Sea Catchment – Observation System (BSC-OS). It exposes a set of applications for data management, analysis and visualization and a complete server and client side development framework (wiki like) to create Web contents. The core of the portal relies on the hydrological semi distributed SWAT code to model the water cycle and predict the effect of management decisions on water, sediment, nutrient and pesticide yields on large river basins. Furthermore, BASHYT aims at quantifying the interconnectedness between (human and natural) pressures and states of water body receptors at different space and time scales. The aim is to enhance environmental management capacity to assess water resource and to share and process large amounts of key environmental information. Within an experimental and innovative programming environment, modules have been developed to run near real-time applications based on numerical solvers (SWAT is just one example), run pre- and post-processing codes, query and map results through the Web browser. A set of web OGC services and a complete Application Programming Interface (API) are also exposed by the portal. We expect to improve the ways in which land management systems can operate and improve model usability to aid in making management decisions and watershed-scale modeling.

Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO₂, showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance.

Scientific portals are key components of many large-scale Earth Science projects. Through the integration of user-friendly web interfaces within the same environment, researchers and scientists can securely and transparently access to data and computational infrastructures, services, applications, etc. Such web environments provide centric gateways to applications based on workflow and dataflow services and a strong support in accessing to quality information.

Emerging technologies such as the Internet of Things (IoT), big data, mobile devices, high-performance computing, and advanced modeling are reshaping urban management. When integrated with conventional tools, these innovations enable smarter governance—particularly in air quality control—improving public health and quality of life. Yet, urban expansion driven by economic growth continues to worsen pollution and its health impacts. This study presents AERQ, a decision support system (DSS) designed to address urban air quality challenges through real-time sensor data and the AERMOD dispersion model. Applied to Cagliari (Italy), AERQ is used to evaluate key traffic-related pollutants (CO, PM, NO₂) and simulate mitigation scenarios. Results are delivered via a user-friendly web-based platform for policymakers, technicians, and citizens. AERQ supports data-driven planning and near real-time responses, demonstrating the potential of integrated digital tools for sustainable urban governance. In the case study, it achieved 10 m spatial and 1 h temporal resolution, while reducing simulation time by 99%—delivering detailed five-year scenarios in just 15 h.