Incorporating sewage sludge (SS) into soils presents a cost-effective and environmentally friendly option compared to conventional farming practices. However, SS could be perceived as a double-edged sword, as it may contain a broad spectrum of contaminants, such as heavy metals (HMs), microplastics (MPs), Pharmaceuticals in the Environment (PIE), and personal care products (PSPs), raising concerns for soil health, water resources, food safety, and human health. Council Directive 86/278/EEC, which regulates SS application in agriculture, specifies limits for six HMs but has not undergone substantive revisions since its inception in 1986, until the release of the updated working document SWD-2023-{final 158}. This study critically examines the legislative landscape across the European Union (EU) Member States (MSs), identifying heterogeneity in implementation, regulatory gaps, and the absence of thresholds for emerging contaminants. The results reveal significant disparities in the permissible concentrations of HMs across MSs and in comparison to international guidelines established by the Food and Agriculture Organization (FAO) and the World Health Organization (WHO). Furthermore, the absence of regulatory measures for MPs, PIE, and other common soil pollutants underscores critical deficiencies in the current framework. These inconsistencies contribute to varying levels of soil health across the EU and highlight the need for a harmonized approach. The findings of this study highlight the imperative for a comprehensive overhaul of the EU legislative framework governing SS application. As evidenced, the establishment of harmonized contaminant thresholds, rigorous monitoring protocols, and regulatory provisions for emergent pollutants is essential for addressing the identified regulatory gaps, enhancing legislative coherence, and promoting sustainable agricultural practices aligned with the EU’s environmental and public health objectives.

Accurate and spatially detailed soil information is essential for supporting sustainable land use planning, particularly in data-scarce regions such as Cyprus, where soil degradation risks are intensified by land fragmentation, water scarcity, and climate change pressure. This study aimed to generate national-scale predictive maps of key soil health descriptors by integrating satellite-based indicators with a recently released geo-referenced soil dataset. A machine learning model was applied to estimate a suite of soil properties, including organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus and provide high resolution insights into degradation processes, such as organic carbon loss, and salinization risk. These outputs provide added value for identifying priority zones for soil conservation and evidence-based land management planning. While predictive uncertainty is greater in areas lacking ground reference data, particularly in the northeastern part of the island, the modeling framework demonstrates strong potential for a national-scale soil health assessment. The outcomes are directly relevant to ongoing soil policy developments, including the forthcoming Soil Monitoring Law, and provide spatial prediction models and indicator maps that support the assessment and mitigation of soil degradation.

Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center – Kellogg Soil Survey Laboratory (NSSC-KSSL) mid infrared (MIR) spectral library, are enormously v...