Maria Silvia Binetti

Soil contamination monitoring in industrialized regions requires accurate, spatially continuous assessments. We present an integrated remote sensing framework for predicting concentration of soil Potentially Toxic Elements (Cd, Be, V, Cr, As, Co), which were selected based on their significant correlations with hyperspectral and multispectral signatures observed in preliminary exploratory chemical analysis. The framework integrates PRISMA hyperspectral, Sentinel-2 multispectral, and DEM-derived topographic data, tested near the industrial area of Taranto (southern Italy), a priority site for environmental risk assessment.Our methodology integrates heterogeneous satellite data through systematic preprocessing, spectral index computation, morphometric feature extraction, and spatially feature selection. A correlation-based selection algorithm with a spectral distance constraint (∆λ

Increasing urbanisation and the intensification of environmental and climate challenges require a review of governance models and tools supporting urban and territorial planning. The Twin Transition concept (green and digital) requires the integration of advanced monitoring and simulation systems. In this context, Digital Twins (DTs) have evolved from static virtual replicas to dynamic urban intelligence systems. Thanks to the integration of IoT sensors and artificial intelligence algorithms, DT enables the transition from a descriptive to a prescriptive approach, supporting climate uncertainty management and real-time territorial governance. The ability to integrate multi-source data and provide high-resolution site-specific representations makes these tools strategic for planning, resource management, and the assessment of urban and peri-urban resilience. The contribution comparatively analyses different digital twin frameworks, with particular attention to their applicability in highly complex environmental contexts, such as the city of Taranto. As a Site of National Interest, Taranto requires models capable of integrating industrial pollutant monitoring with urban regeneration and biodiversity protection strategies. The study assesses the potential of DT as predictive models to support governance for more sustainable, adaptive, and resilient cities.

This study addresses the global challenge of superficial soil contamination by heavy metals, focusing on differentiating natural geogenic sources from anthropogenic contributions in complex industrial–urban environments. We develop an integrated geostatistical and multivariate framework combining soil metal concentration analysis with AERMOD atmospheric dispersion modeling using a comparative multi-model machine learning approach (including Extreme Gradient Boosting, Random Forest, and Ridge Regression). Applied to the industrialized area of Taranto, Southern Italy, this approach incorporates spatial autocorrelation and multiple environmental predictors to identify contamination patterns and sources. The results reveal variable predictive accuracy across metals, with RF generally outperforming the other algorithms. The model achieved its highest performance for copper (R² = 0.58, RMSE = 25.82), Tin (R² = 0.53, RMSE = 5.95), and chromium, while showing instability for others. These disparities highlight the differential influence of remote sensing data on contamination mapping. The framework advances the quantitative assessment of soil pollution by linking atmospheric deposition and spatial processes with causal interpretability.

In highly urbanised and industrialised settings, managing environmental pressures and enhancing urban resilience demand integrated, spatially explicit approaches. This study presents a methodological framework that integrates topographic data, land cover information, and open geodata to produce a high-resolution vulnerability map. A multi-criteria analysis was performed using indicators such as land use, population density, proximity to emission sources, vegetation cover, and sensitive services (e.g., schools and hospitals). The result is a high-resolution vulnerability map that classifies the urban, peri-urban, and coastal zones into five levels of environmental risk. These evaluation levels are derived from geospatial analyses combining pollutant dispersion modelling with land-use classification, enabling the identification of the most vulnerable urban zones. These findings support evidence-based planning and can guide local governments and environmental agencies in prioritising Nature-based Solutions (NBSs), enhancing ecological connectivity, and reducing exposure for vulnerable populations.

The 2030 Agenda for Sustainable Development, approved by the international community, represents a global strategic guide with 17 Sustainable Development Goals (SDGs) and 169 interconnected targets, aimed at promoting equitable economic, social and environmental development. In this context, innovation and research can play a crucial role in supporting policies for the achievement of SDGs, especially at local and regional levels. This article reports what was developed in the Puglia Region according to a double action. On the one hand, the creation of an IT platform to improve collaboration between legislative institutions and research centres facilitates the collection and transfer of data as best practices to support political decisions. On the other hand, practical experimentation on issues related to regional development is conducted, with particular attention to sustainability-oriented partnerships, legislative needs, and knowledge of the territory. The new information system, based on a geo-database and developed entirely with open source software, collects regional regulatory data on SDGs, cooperation projects, and technical and scientific documents contributing to the knowledge of the application of plans and programmes applied to the territory. This tool, in addition to mapping relevant projects, represents an important resource for monitoring progress and supporting sustainable development policies, facilitating the sharing of information between local, national, and European actors.

This paper examines land management technologies to enhance environmental monitoring more efficiently. The study highlights the interactions between human activities and environmental systems with a data-driven environmental monitoring approach. There are many human pressures, such as pollution, land degradation, and habitat loss, negatively impacting soil health. The methodology proposed improves soil status assessments in response to evolving environmental pressures by utilizing satellite imagery and predictive modeling. The integration of Sentinel-2 imagery, the calculation of various spectral indices (NDVI, NBR, NDMI, EVI, SAVI) at different time intervals, and the application of the Isolation Forest algorithm are employed in this study to determine the specific area that is affected by the environmental issue. The chosen algorithm was favored due to its superior performance in handling high-dimensionality data, enhanced computational efficiency, provision of interpretable results, and insensitivity to disparities in class distribution. This study analyzes two separate study cases at different scales. The first involves wildfire identification achieving an overall accuracy of 98%. The second focuses on the expansion areas to pre-existing quarries with an overall accuracy of 95%. The NBR proved most effective in delineating burned areas, whereas the EVI generated the most remarkable results in the quarry case study. This approach provides an effective and scalable tool for environmental monitoring, supporting sustainable management policies, and strengthening ecosystem resilience.

This study aims to enhance natural heritage through detailed monitoring aimed at evaluating ongoing environmental dynamics and anthropic impacts on fragile coastal ecosystems, with particular attention to dune ecosystems and back-dune ponds in Southern Italy. The integration of remote sensing technologies, such as thermal cameras and geospatial data, has made it possible to identify underground water sources that are useful for characterizing and monitoring the water regime of the targeted area. Through modelling software, different methods of assessing the environmental state, aimed at identifying the best sustainable practices that can be implemented in these fragile ecosystems, are also proposed. The presented multidisciplinary approach demonstrates how science and technology can support the sustainable management of protected areas, with positive implications for environmental protection and local development, and the adoption of best practices, inspired by international models, that can promote the conservation of biodiversity and the valorization of historical heritage.

This chapter provides an overview of Directive 91/676/EEC, which aims to protect water resources from pollution by nitrates from agricultural source. An important aspect of this Directive is the identification and delimitation of Nitrate Vulnerable Zones (NVZs), areas identified as being at high risk of nitrate pollution and subject to specific regulatory measures. Furthermore, the chapter reports a methodology for slope calculation, which is crucial for assessing runoff potential and subsequent nitrate leaching and comprehensive case studies on livestock manure management technologies, highlighting innovative practices for reducing environmental impact.

This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions.