The investment estimation of the helium extraction project from natural gas is a crucial step in economically obtaining helium from both domestic and international projects. This article employs Aspen HYSYS to simulate the process and estimate the investment levels of Linde and Exxon Mobil integrated helium extraction processes. We investigate the influence of feed composition and processing capacity on investment costs and product returns. The results indicate that higher helium content of feed correlates with increased equipment investment costs and total capital cost (CAPEX), and that the Linde integrated process is significantly more sensitive to changes in helium content of feed than the Exxon Mobil integrated process. As the helium content of feed rises, the product returns of the two processes are evidently improved, leading to reduced investment payback periods. Both techniques exhibit favorable payback periods when the feed helium content exceeds 0.5 vol%. Nevertheless, elevated nitrogen content in the feed notably escalates the equipment investment costs and total capital costs. Furthermore, an increase in the processing capacity of feed gas leads to a nonlinear increase in total capital costs and annual operating costs. However, the cost per unit of helium extraction diminishes with increasing capacity. In general, the Linde integrated process requires higher separation energy consumption in comparison with the Exxon Mobil integrated process at similar processing capacities. Moreover, the sensitivity analysis shows that helium breakeven price is strongly affected by the price of both LNG and feed gas.

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.