Privacy-preserving feature selection allows identifying more important features while ensuring data privacy, thus enhancing data quality. Secure multiparty computation (MPC) is a cryptographic method that allows effective data processing without a trusted third party. However, most MPC-based feature selection schemes overlook the correlation between features and perform poorly for model training when handling datasets containing both numerical and categorical attributes.

Forest ecosystems stand as indispensable regulators of the planet’s climate, actively influencing atmospheric greenhouse gas (GHG) emissions and thereby affecting global warming. A recent study by researchers at the University of Debrecen provides a comprehensive evaluation of these emissions from various sources within forested landscapes. The investigation assesses their individual contributions to global warming potential (GWP), delivering crucial insights for shaping climate policies, advancing carbon accounting, and implementing sustainable forest management practices. This work is essential for developing more precise strategies to mitigate climate change and deepening our scientific understanding of ecosystem-climate dynamics.

To achieve its objectives, the research employed a rigorous analytical framework, utilizing comprehensive data from the EDGAR—Emissions Database for Global Atmospheric Research, spanning from 1990 to 2022. This extensive dataset enabled the team to meticulously analyze emissions of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) originating from deforestation, forest fires, and natural processes such as organic soil decomposition. The study leveraged time series analysis and an ARIMA model to identify trends, project emission trajectories until 2030, and quantify CO₂ equivalent emissions for each category. Further, correlation analysis illuminated the intricate relationships between various emission sources, offering a holistic perspective on terrestrial carbon dynamics.

A comprehensive new analysis of South Africa's environmental footprint reveals a complex and often contradictory relationship between development and pollution. Researchers Frank Ranganai Matenda, Helper Zhou, and Mabutho Sibanda from the University of KwaZulu-Natal, alongside Asif Raihan of the National University of Malaysia, examined three decades of national data to untangle the key drivers of carbon dioxide (CO₂) emissions. The investigation, spanning from 1990 to 2020, exposes how economic progress, globalization, and even technological innovation are currently contributing to rising emissions, while highlighting the significant potential of renewable energy to reverse this trend.

Applying biochar to soil is a recognized strategy for combating climate change, primarily by locking away carbon for long periods. Yet, its broader impact is complex; under different conditions, biochar can either suppress or unexpectedly release other potent greenhouse gases like nitrous oxide and methane from the soil. This inconsistency has been a significant barrier to its widespread adoption. A new set of predictive models developed by researchers Beatriz A. Belmonte, Raymond R. Tan, and their colleagues at the University of Santo Tomas and De La Salle University brings clarity to this issue. The team created a system to predict how soils will respond to biochar, offering a way to tailor its application for maximum climate benefit.

A collaborative team of researchers from the University of Science and Technology, Beijing, and the Chinese Research Academy of Environmental Sciences has provided an unprecedented molecular-level view into the water quality of urban rivers. The investigation focused on dissolved organic matter (DOM), a complex mixture of carbon-based compounds that influences aquatic ecosystems and drinking water safety. By analyzing the intricate chemical makeup of DOM, scientists can trace its origins, whether from natural soil and plant decay or from human-caused pollution. This new work offers a powerful diagnostic approach for understanding the health of waterways in densely populated areas.

The investigation centered on two vital Beijing waterways with differing roles and surrounding environments: the Yongding River (YDH) and the Beiyun River (BYH). The YDH, known as Beijing's "mother river," primarily serves water supply functions and flows through mountainous, forested terrain. In contrast, the BYH courses through the city’s urban sub-center, receiving significant amounts of domestic sewage and agricultural runoff. This intentional comparison allowed the scientific team to isolate how distinct landscapes and anthropogenic pressures imprint unique chemical signatures on the rivers’ dissolved organic matter pools.