AN INTER-COMPARISON OF THE SPATIAL AND TEMPORAL CHARACTERISTICS OF CO OVER HIGH FIRE REGIONS BASED ON MOPITT AND GFED

Black carbon's climate impact is highly uncertain because its radiative effect depends on particle size, mixing state, and column loading.  This study combines satellite data, physics-based simulations, and machine learning to estimate black carbon forcing across contrasting regions. The same black carbon amount can warm or cool the atmosphere depending on local aerosol properties. The machine learning framework provides a fast, transferable tool for regional climate assessment.

There are always gaps in satellite records of air pollution because of factors such as clouds and operating conditions. We used a physics-aware deep learning method, producing a gapless 0.05° hourly dataset of tropospheric nitrogen dioxide over Asian pollution hotspots from 2019 to 2024. The dataset can capture sub-daily changes, revealing how traffic, industry, and clean-air policies shaped pollution, supporting health and environment studies.

This study examines how black carbon particles from the coal industry influence regional climate by absorbing sunlight. Based on ground measurements and modeling, we find that conventional approaches, which oversimplify particle size and structure, which could leading a bias in terms of their warming effect. Our results highlight that more realistic particle characterizations are crucial for improving climate predictions in industrial regions.

This study provides TROPOMI (TROPOspheric Monitoring Instrument) with a new methane emission estimation method that can accurately identify emission sources. Our results generate non-negative emission datasets using objective selection and filtering methods. The results include lower minimum emission thresholds for all power grids and fewer false positives. The new method provides more robust emission quantification in the face of data uncertainty, going beyond traditional plume identification and background subtraction.

Black carbon (BC), emitted from fires, industry, and fossil fuels, warms the climate and worsens air quality. Due to complex physical and optical properties, BC remains poorly constrained. We combined satellite observations with physical models to build a daily, high-resolution, decadal global dataset of BC mass, number, size, and mixing details. The data contains both known sources and new hotspots. These results can support climate models, satellite products, and pollution mitigation.