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Crops could accumulate trace metals by soil-root transfer and foliar uptake from atmospheric fallout, and an accurate assessment of pollution sources is a prerequisite for preventing heavy metal pollution in agricultural products. In this study, we examined Pb isotope rates to trace the sources of Pb in wheat grain grown in suburbs. Results showed that, even in zones with scarcely any air pollution spots, atmospheric fallout was still a considerable source of Pb accumulation in wheat. The concentration of Pb in wheat grain has poor correlation with that in farm soil. The Pb concentration in wheat grains with dust in bran coat was significantly higher than that in wheat grains, which indicates that Pb may accumulate by foliar uptake. The Pb isotope rate has obvious differences between the soil and atmospheric fallout, and scatter ratio is significantly closer between the wheat grain and atmospheric fallout. Atmospheric fallout is a more significant source of Pb concentration in wheat grains than in soil. As far as we know, this is the first study on the main sources of lead in grain crop (wheat) samples with isotope. This study aims to improve our understanding of the translocation of foliar-absorbed metals to nonexposed parts of plants.

Cadmium pollution of soil threatens the environmental quality and human health. Phytoremediation of cadmium-contaminated soil has attracted global attention in recent decades. This study aimed to conduct a comprehensive and systematic review of the literature on phytoremediation of cadmium-contaminated soil based on bibliometric analysis. A total of 5494 articles published between 1994 and 2021 were retrieved from the Web of Science Core Collection. Our knowledge mapping presented the authors, journals, countries, institutions, and other basic information to understand the development status of phytoremediation of cadmium-contaminated soil. Based on a keyword cluster analysis, the identified major research domains were “biochar”, “Thlaspi caerulescens”, “endophytic bacteria”, “oxidative stress”, “EDTA”, and “bioconcentration factor”. Overall, this study provided a detailed summary of research trends and hotspots. Based on the keyword co-occurrence and burst analysis, the core concepts and basic theories of this field were completed in 2011. However, the pace of theoretical development has been relatively slow. Finally, future research trends/frontiers were proposed, such as biochar addition, rhizosphere bacterial community manipulation, cadmium subcellular distribution, and health risk assessment.

Electricity price forecasting in Europe presents unique challenges due to increasing renewable generation variability, market integration, and the continent's physically interconnected power system. While recent advances in foundation models have led to substantial improvements in general time series forecasting, most existing approaches do not incorporate prior graph knowledge from the transmission topology, which can limit their ability to exploit meaningful cross-region dependencies in interconnected power systems, motivating a domain-specific foundation model. In this paper, we address this gap by first introducing a comprehensive and up-to-date dataset across 24 European countries (38 regions), spanning from 2022-01-01 to 2026-01-01. Building on this groundwork, we propose PriceFM, a probabilistic foundation model pretrained on this large dataset. Specifically, PriceFM maps each region's price and exogenous features, including load, solar, and wind generation forecasts, into a comparable latent embedding via a shared Mixture-of-Experts (MoE) projection layer, then injects prior graph knowledge by constructing a sparse graph mask derived from transmission topology. Across a large-scale European benchmark, PriceFM achieves strong performance and demonstrates superior generalization compared with multiple competitive baselines. The results highlight the value of topology-guided forecasting with increasing renewable generation and strong cross-border interconnections. The methodology is available at: https://runyao-yu.github.io/PriceFM/.

Electricity price forecasting in Europe presents unique challenges due to the continent's increasingly integrated and physically interconnected power market. While recent advances in deep learning and foundation models have led to substantial improvements in general time series forecasting, most existing approaches fail to capture the complex spatial interdependencies and uncertainty inherent in electricity markets. In this paper, we address these limitations by introducing a comprehensive and up-to-date dataset across 24 European countries (38 regions), spanning from 2022-01-01 to 2025-01-01. Building on this groundwork, we propose PriceFM, a spatiotemporal foundation model that integrates graph-based inductive biases to capture spatial interdependencies across interconnected electricity markets. The model is designed for multi-region, multi-timestep, and multi-quantile probabilistic electricity price forecasting. Extensive experiments and ablation studies confirm the model's effectiveness, consistently outperforming competitive baselines and highlighting the importance of spatial context in electricity markets. The project page is at: https://runyao-yu.github.io/PriceFM/.