Methane is a powerful greenhouse gas, but its sources remain uncertain in many regions. The isotope fingerprints of methane are diagnostic of its sources, yet their source end-members are poorly constrained for South Asia. Here we determined the methane isotope signal for major sources in South Asia and found these to differ from global averages. Improved regional-specific isotope source fingerprints will help to improve top-down assessments of methane budgets and climate mitigation strategies.

This study uses data and machine learning to better estimate methane emissions from a major landfill in South Korea. By considering local weather conditions like temperature and rain, the research improves how landfill methane is tracked over time. The results help us understand how climate affects emissions and provide tools that can be used worldwide to improve greenhouse gas monitoring and climate action planning.

Geostationary observations of NH₃ provide an unprecedented opportunity to monitor spatial and temporal variations in emissions and their evolution throughout the day. Using 3 years of observations from FY-4B/GIIRS over East Asia, we demonstrated the enhanced capability of geostationary observations to identify emission sources and capture daytime variations associated with agricultural activities. This shows the potential of future geostationary satellites for monitoring air quality globally.

Atmospheric new particles formation plays an important role in air quality and climate change, but it does not always appear even in the similar situation. Using ground measurements and vertical observations, we found this process occurs only when the source increases while the sink weakens at the same time, which is mainly controlled by the development of the boundary layer. The finding helps us better understand particle formation in complex atmospheric environments.

We analyzed 22 years of satellite observations to see how small-scale atmospheric waves and the OH emissions change across seasons and regions. Both show clear repeating patterns and are closely linked, revealing how energy moves through the upper atmosphere. These results provide a long-term baseline that can improve computer models used to study weather, climate, and atmospheric change.

This study uses 1 km resolution Weather Research and Forecasting model coupled with 
Chemistry (WRF-Chem) simulations to investigate how Lake Chaohu affects fine 
particulate matter (PM_2.5) in Hefei. The lake shows a diurnal reversal, increasing 
daytime pollution by secondary aerosol formation and storage zones with suppressed 
mixing and low deposition, while purifying urban air at night through enhanced vertical 
mixing. Lake emission treatment affects lake-urban air quality assessments.

Secondary organic aerosol (SOA) constitutes a major component of atmospheric aerosol that models must account for to assess how human activities influence air quality, climate, and public health. We find substantial differences in how current air quality models represent SOA highlighting a lack of consensus within the modelling community. Our findings emphasize the need to recognize the limitations of current SOA schemes in the context of air quality management and policy development.

High-resolution Large-Eddy Simulations (LES) are used to examine how aerosols affect liquid water path (LWP) sensitivity in non-precipitating liquid clouds. Two cases, 1985 (high aerosol) and 2013 (low aerosol), show a non-linear N_d–LWP relationship with a shift from positive to negative sensitivity at higher N_d under high aerosol loading. Enhanced cloud-top entrainment, evaporation, and heating drive this shift, highlighting aerosol impact on cloud microphysics and thermodynamics.

We developed a machine learning approach to map daily air pollution across China at high resolution, covering six major pollutants. Our results reveal how different pollutants respond differently to changes in human activity and emissions, uncovering the underlying chemical and atmospheric processes. This study provides detailed evidence of air pollution patterns and interactions, offering insights that can guide more effective strategies to protect air quality and public health.

We develop and implement a prognostic model of the atmospheric humidity variance in a global climate model to improve cloud prediction. Results are systematically compared with a high-resolution model using automatic tuning tools. We show consistency with earlier work performed with a diagnostic model while providing significant improvements in the description and understanding of the atmospheric humidity distribution by highlighting the role of the air detrained from thermals.

Tiny aerosol particles help form cloud droplets and can affect rainfall and climate, but their behavior is hard to predict. We used aircraft measurements over the ocean near the Azores and over the central United States to compare clean and polluted air. We found that particle size and chemical makeup together control how easily clouds form, and organic-rich particles often reduce droplet formation. These results can improve how weather and climate models represent clouds.

NO_x emissions from aircraft affect the climate indirectly, by changing greenhouse gas concentrations. We explore whether the NO_x emissions from aviation would have a different effect in different potential future climate states, i.e. a high pollution and low pollution case. The three models we use show varied responses for how this background state alters the climate effects of the NO_x emissions. This shows the continuing need to improve our understanding of non-CO₂ aviation impacts.

The vertical evolution of microphysical cloud properties in low-level mixed-phase clouds during a marine cold air outbreak in the Arctic is analyzed based on measurements collected during the HALO–(𝒜𝒞)³ campaign. In particular, pseudo-vertical profiles of cloud thermodynamic phase and the cloud droplet size are constructed. The measured vertical profiles are compared to predictions from an entraining parcel model to investigate the influence of ice processes on supercooled liquid water droplets.

Air quality in rapidly growing cities is difficult to model because emissions vary during the day and are often poorly constrained. We demonstrate a methodology for constraining urban emissions using hourly satellite observations combined with high-resolution air quality modeling in Bangkok. We find that emissions inventories likely overestimate nitrogen dioxide emissions, and that updated emissions improve agreement between the model and independent aircraft and ground-based observations.

We studied how airborne particles like smoke affect a pristine Amazon rainforest. Using long-term data, we found that high aerosol pollution reduces the heat and water vapor released by the forest, causing a cooling effect. Surprisingly, it also substantially boosts the forest's carbon dioxide absorption by scattering sunlight, which helps plants with photosynthesis. This shows that aerosols significantly alter the Amazon's microclimate and its crucial role in the global carbon and water cycles.

We studied how forests in Finland release natural gases that affect air quality and climate. Existing models strongly overestimated these emissions because they used overly simple forest descriptions. By adding detailed information on tree species, we greatly improved agreement with real measurements. This means more realistic estimates of particle formation in the air. Our results show that accurate forest data are essential for reliable climate and air quality predictions.

We study the evolution of ozone in the Arctic over the 2000–2024 period in the stratosphere (about 10 to 50 km) to assess the expected recovery of the ozone layer following the diminution of ozone-depleting substances. We merge ground-based data sets within spatially coherent regions to reduce uncertainties and we obtain positive trends for the total column everywhere in the Arctic and for the middle and upper stratosphere over Canada, but no significant trends in the lower stratosphere.

We use a global atmospheric chemistry and climate model to study the efficiency and effectiveness of the deep convective transport of CO, NH₃ and SO₂ from the planetary boundary layer into the Asian summer monsoon anticyclone. We find that in contrast to CO and NH₃, the SO₂ enhancements within the anticyclone are very weak. The wet scavenging over South Asia is more effective for SO₂ than NH₃ at reducing their amounts reaching the Tibetan Plateau and the Asian summer monsoon anticyclone.

Mountain waves are excited when wind flows over elevated terrain. These waves travel through the atmosphere and alter the winds locally when they break, thereby impacting global-scale dynamic phenomena. Understanding mountain waves and their interactions, they need to be identified in measurement data or model simulations. This study provides a method for disentangling the mountain waves from atmospheric fluctuations of other origin in three-dimensional temperature data.

The Aura/MLS (Aura satellite/Microwave Limb Sounder) observations of geopotential height and temperature show lunar semimonthly variations which inform about the amplitude profiles and the vertical phase progression of the lunar tide in the middle atmosphere. The observed lunar tide are compared to a simulation of the lunar tide by Geller.