Fourier-transform infrared (FTIR) spectroscopy is a rapid and nondestructive technology for monitoring atmospheric quality. The identification of each component from the FTIR spectra is a prerequisite for the accurate quantitative analysis of gaseous pollutants. Due to the overlap of different gas absorption peaks and the interference of water vapor in the actual measurement, the existing identification methods of gas spectra have drawbacks of low identification rate and the inability to carry out real-time online analysis in atmospheric quality monitoring. In this work, independent component analysis (ICA) is applied to the spectral separation of heavily overlapped spectra of gaseous pollutants. The proposed method is validated by the analysis of mixture spectra obtained in laboratory and actual atmospheric spectra collected from stationary source. The average time consumption of separation process is less than 0.2 seconds, and the identification rate of experimental gases is up to 100%, as shown by the results of peak searching and the analysis of the correction coefficient between the separated spectra and the standard spectra database. The identification results of actual atmospheric spectra demonstrated that the proposed method can effectively identify the gaseous pollutants whose concentration changes in the measured spectra, and it is a promising qualitative spectral analysis tool that can shorten the identification time, as well as increase the identification rate. Therefore, this method can be a useful alternative to traditional qualitative identification methods for real-time online atmospheric pollutant detection.

Remote sensing image dehazing remains a formidable challenge due to complex atmospheric scattering and large-scale spatially varying degradation, which severely compromise fine-grained surface details. While recent diffusion-based restoration frameworks, such as DiffIR, have achieved remarkable efficiency by injecting compact diffusion priors into deterministic networks, they typically rely on a monolithic global Image Prior Representation (IPR). However, such a global design is suboptimal for the dehazed results of remote sensing imagery, where haze distribution exhibits strong spatial heterogeneity and scale dependency. To address this limitation, this paper presents the Hierarchical and Scale-Adaptive Diffusion Prior (HS-DiffIR) framework. Specifically, Hierarchical Image Prior Representation decomposes the holistic diffusion latent into multi-scale priors aligned with the hierarchical stages of the restoration network. Such a design facilitates fine-grained, scale-aware guidance by projecting the compact global latent into layer-specific representations, thereby bypassing the computational burden of high-dimensional generative modeling. Complementing this, the Scale-Adaptive Injection mechanism utilizes lightweight learnable coefficients to dynamically modulate the influence of diffusion priors across different feature scales, allowing the network to adaptively balance global semantic consistency and local detail recovery under dense-haze conditions. Evaluations on remote sensing benchmarks confirm that HS-DiffIR generally outperforms the DiffIR baseline. The method yields superior quantitative metrics (particularly PSNR) at a marginal computational cost while demonstrating robust detail restoration in regions subject to severe, spatially variant haze.