Tropospheric ozone (O₃), a secondary pollutant of mounting global concern, emerges from complex, nonlinear photochemical reactions involving nitrogen oxides (NO_x) and volatile organic compounds (VOCs) under dynamically evolving meteorological conditions. Accurately characterizing and effectively regulating O₃ formation necessitates not only precise and multi-dimensional precursor observations but also modeling frameworks that are structurally coherent, chemically interpretable, and sensitive to regime variability. Despite significant technological progress, current research remains markedly fragmented: observational platforms often operate in isolation with limited vertical and spatial interoperability, while modeling paradigms—ranging from mechanistic chemical transport models (CTMs) to data-driven machine learning approaches—frequently trade interpretability for predictive performance and struggle to capture regime transitions across heterogeneous environments. This review provides a dual-perspective synthesis of recent advances and enduring challenges in the VOC–O₃ research landscape. We first establish a typology of ground-based, airborne, and satellite-based VOC monitoring systems, evaluating their capabilities, limitations, and roles within a vertically structured sensing architecture. We then examine the evolution of O₃ modeling strategies, from empirical and semi-mechanistic models to hybrid frameworks that integrate physical knowledge with algorithmic flexibility. By diagnosing the structural decoupling between observation and inference, we identify key methodological bottlenecks and advocate for a system-level redesign of the VOC–O₃ research paradigm. Finally, we propose a forward-looking framework for next-generation atmospheric governance—one that fuses cross-platform sensing, regime-aware modeling, and policy-relevant diagnostics into an integrated, adaptive, and chemically robust decision-support system.

Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling. Unlike conventional models that treat spectral–spatial features uniformly, UniHSFormer-X dynamically modulates information flow based on class-aware affinities, enabling precise delineation of field boundaries and robust recognition of spectrally entangled crop types. Evaluated on three UAV-based benchmarks—WHU-Hi-LongKou, HanChuan, and HongHu—the model achieves up to 99.80% overall accuracy and 99.28% average accuracy, outperforming state-of-the-art CNN, ViT, and hybrid architectures across both structured and heterogeneous agricultural scenarios. Ablation studies further reveal the critical role of semantic routing and prototype projection in stabilizing model behavior, while parameter surface analysis demonstrates consistent generalization across diverse configurations. Beyond high performance, UniHSFormer-X offers a semantically interpretable architecture that adapts to the spatial logic and compositional nuance of agricultural imagery, representing a forward step toward robust and scalable crop classification.

Petroleum hydrocarbon contamination poses a persistent threat to soil quality and ecosystem functioning, while conventional remediation approaches are often costly and difficult to sustain over the long term. This study evaluated garden waste biochar as a sustainable amendment for the remediation of petroleum-contaminated soil and investigated its effects on TPH removal, soil physicochemical properties, and bacterial community changes. Biochar produced from garden waste at 600 °C was applied at 1% and 2% (w/w) to soil containing approximately 20,000 mg·kg⁻¹ TPH, and changes were monitored over 360 days. High-throughput 16S rRNA gene sequencing and redundancy analysis were combined with measurements of soil properties and TPH concentrations. Compared with the petroleum-contaminated control, biochar amendment substantially enhanced TPH removal, with removal efficiencies increasing from 24.44% to 44.26% and 52.93% under 1% and 2% biochar treatments, respectively. After 360 days, TPH concentrations decreased to 10,831.68 and 9102.07 mg·kg⁻¹ in the 1% and 2% biochar treatments, respectively. Biochar also significantly altered bacterial richness and community composition, with significant changes in the Ace, Chao1, and Sobs indices and shifts in dominant bacterial phyla. Soil nutrient availability and bacterial community structure were closely associated with environmental changes during remediation. Overall, garden waste biochar shows potential as a sustainable strategy that simultaneously promotes biomass waste valorization and petroleum-contaminated soil remediation.