Fine particulate matter (PM) release is regulated by environmental policies in most countries. This study investigated long–term trends in the mass extinction efficiency (Q_e) of aerosols in Northeast Asia. For this purpose, the Q_e was calculated using visibility, PM_2.5 recorded between 2015 and 2020, and PM₁₀ recorded between 2001 and 2020 at eight Korean sites. The Q_e of PM₁₀ (Q_e,10) showed an increasing trend with 0.06~0.22 (m²/g)/yr in seven cities except for Jeju. The Q_e of PM_2.5 (Q_e,2.5) also showed an increasing trend with 0.28–2.47 (m²/g)/yr in all cities. In this study, PM₁₀ and PM_2.5, were divided into low, moderate, and high concentrations, and the Q_e value change by year was examined. Q_e,10 showed a tendency to decrease at low concentrations (19–21 μg/m³). However, at moderate (69–71 μg/m³) and high concentrations (139–141 μg/m³), Q_e,10 increased in most regions. Q_e,2.5 showed an increasing trend at low concentration (9–11 μg/m³), moderate concentration (29–31 μg/m³), and high concentration (69–71 μg/m³), except for Suwon and Pohang, where data were insufficient for analysis. Both Q_e,10 and Q_e,2.5 showed an increasing trend. The increase in Q_e indicated that the visibility-impairing effect of PM can increase even if the same concentration of PM is present. The visibility-impairing effects of PM vary based on the composition, size and other characteristics of the particles in the atmosphere at a given point in time and not simply the quantity of particles. This means that reducing the quantity of particles does not reliably produce a proportionate improvement in visibility. Air quality policies must take the variable nature of PM particles and their effect on visibility into account so that more consistent improvements in air quality can be achieved.

Ammonia (NH₃), as a vital component of the nitrogen cycle, exerts significant influence on the biosphere, air quality, and climate by contributing to secondary aerosol formation through its reactions with sulfur dioxide (SO₂) and nitrogen oxides (NO_x). Despite its critical environmental role, NH₃’s transient atmospheric lifetime and the variability in spatial and temporal distributions pose challenges for effective global monitoring and comprehensive impact assessment. Recognizing the inadequacies in current in situ measurement capabilities, this study embarked on an extensive analysis of NH₃’s temporal and spatial characteristics over East Asia, using the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-B satellite from 2013 to 2024. The atmospheric NH₃ concentrations exhibit clear seasonality, beginning to rise in spring, peaking in summer, and then decreasing in winter. Overall, atmospheric NH₃ shows an annual increasing trend, with significant increases particularly evident in Eastern China, especially in June. The regional NH₃ trends within China have varied, with steady increases across most regions, while the Northeastern China Plain remained stable until a recent rapid rise. South Korea continues to show consistent and accelerating growth. East Asia demonstrates similar NH₃ emission characteristics, driven by farmland and livestock. The spatial and temporal inconsistencies between satellite data and global chemical transport models underscore the importance of establishing accurate NH₃ emission inventories in East Asia.

Atmospheric methane is the second-largest contributor to radiative forcing among long-lived greenhouse gases, and characterizing its spatiotemporal distribution is essential for identifying sources and evaluating mitigation policies. Methane emissions are most directly reflected in near-surface concentrations, whereas satellite observations retrieve only the column-averaged methane concentrations. This study presents a physics-informed stacking ensemble framework that estimates surface CH₄ directly from TROPOMI SWIR observations. SWIR radiance channels within the 2305–2385 nm band were optimally selected on the basis of their CH₄ information content, including the contribution of the lower atmosphere, as quantified by radiative transfer analysis, and these L1B radiances were combined with L2 products and auxiliary variables governing surface concentration formation. Predictions from machine learning models with different learning schemes were fused through a stacking ensemble that exploited their complementary error structures and outperformed all individual base learners against ground-based in situ observations. For years and stations that were not used in training, the ensemble achieved R² = 0.75 and 0.66, respectively. The selected channels improved estimation performance, and the L1B-only configuration performed comparably without a dedicated CH₄ retrieval product, indicating that the observed spectra carry information relevant to surface CH₄ before retrieval. The reconstructed global fields reproduced large-scale spatial and seasonal variability with stronger regional contrasts than the input column observations, demonstrating that physically informed channel selection combined with machine learning enables satellite-based surface CH₄ estimation.