Conference Agenda

Aerosol size information, typically represented by fine- and coarse-mode aerosol optical depth (fAOD and cAOD), is crucial for understanding anthropogenic emissions and radiative effects. However, satellite-based retrievals suffer from limited labeled data and high uncertainty over land. To address these challenges, we developed a novel deep pretraining framework capable of mining latent representations from unlabeled satellite pixels, thereby enhancing the accuracy and generalization of aerosol size information retrieval. The framework leverages a self-supervised pretraining stage to capture intrinsic spatiotemporal correlations in multispectral satellite data and transfers these latent features to a supervised fine-tuning model. Using MODIS data combined with AERONET observations, our pretrained model achieved a 10% improvement in correlation and a 15% enhancement in regions without ground observations compared to conventional deep-learning models. The retrieved global fAOD from 2001–2020 reveals a significant decreasing trend (−1.39 × 10⁻³ yr⁻¹), with regional differences—most notably, a threefold stronger decline over China than the global average. These results demonstrate that deep pretraining can effectively exploit unlabeled satellite information, bridging the gap between sparse ground networks and dense global observations, and offering a transformative approach for large-scale aerosol characterization and climate studies.

Atmospheric aerosols are a key factor influencing the Earth's radiation balance and climate change, and the accuracy of their retrieval is crucial for environmental monitoring and climate research. FY-4A AGRI, with its high-frequency observation capability, can provide aerosol data at high temporal resolution. Combined with deep learning technology, it enables efficient monitoring of dynamic aerosol variations. This study develops a retrieval algorithm for aerosol optical and microphysical parameters based on the Transformer deep learning model, specifically designed for the FY-4A geostationary satellite. The algorithm achieves multi-parameter collaborative retrieval of aerosol optical depth (AOD), fine/coarse-mode aerosol optical depth (FAOD/CAOD), and single scattering albedo (SSA). This research overcomes the reliance on prior assumptions inherent in traditional physical retrieval methods. By integrating multi-band spectral features, geometric observation parameters, and data from 104 AERONET sites, it significantly enhances retrieval accuracy under the complex surface conditions of East Asia. Experimental results demonstrate high accuracy in validation against AERONET sites, with correlation coefficients of R=0.915 for AOD, R=0.897 for FAOD, R=0.851 for CAOD, and R=0.536 for SSA. Comparative validation of various aerosol product spatial distributions highlights the advantages of the proposed algorithm in capturing aerosol diurnal variations (such as haze dissipation processes) and extreme events (e.g., dust storms and biomass burning). This study provides a new technical approach for regional air quality monitoring and climate effect assessment, advancing the application of China’s geostationary meteorological satellites in aerosol monitoring.

Heavy metal pollution presents a formidable challenge to global ecosystems, threatening biodiversity, soil and water quality, and human health. The atmosphere serves as both a source and long-range conveyor of bioaerosols, complex particles that include bacteria, fungal spores, and dust-bound heavy metals, profoundly influencing biosphere health and ecosystem function. In this study, we investigate atmosphere-biosphere interactions in Pin Valley National Park, a cold desert ecosystem in the Western Himalayas, by analyzing how bioaerosol-mediated deposition of heavy metals shapes vegetation stress and phytoremediation dynamics. Integrating field spectroscopy, in-situ chemical analysis (ICP-MS), and multi-temporal satellite data, we mapped heavy metal hotspots (Pb, Cd, Ni, Cr) and linked them to shifts in vegetation health and thermal indices. We observed significant spatial overlap between elevated metal concentrations likely introduced via long-range atmospheric transport and suppressed vegetation indices. Phytoremediator species such as Brassica juncea and Populus exhibited strong metal uptake, revealing natural biospheric buffering capacity against airborne contaminants. Additionally, iron oxide and hydrothermal indices indicated that soil mineral conditions, modulated by deposition, may influence microbial and root zone dynamics. This multidisciplinary assessment underscores the role of the atmosphere not merely as a depositor but as a dynamic bioreactor influencing terrestrial microbiomes and plant stress responses. By offering a scalable, remote sensing–assisted framework for monitoring ecosystem health and contaminant transport, our work directly supports SDG 13 by identifying atmospheric pathways of pollutant stress under warming trends, contributes to SDG 15 by protecting fragile alpine ecosystems through phytoremediation, and aligns with SDG 17 as an interdisciplinary approach.

The relationship between aerosol optical depth (AOD) and surface PM2.5 concentrations remains a significant difficulty in remote sensing-based air quality assessments due to meteorological conditions and aerosol vertical structure. This relationship is investigated using daily observations from 2021 to 2024 in Sherbrooke, Quebec, Canada.

Ground-based AERONET AOD500 and satellite-based MAIAC AOD at 550 nm are analyzed separately, together with surface PM2.5 measurements from a local PurpleAir sensor. Meteorological parameters such as relative humidity, boundary layer height, temperature, and wind speed are available from ERA5 reanalysis. Vertically resolved aerosol information from MPLNET lidar is used to identify elevated aerosol layers associated with transported wildfire smoke.

The approach combines Pearson and Spearman correlations, partial correlation analysis, multivariate regression, and Random Forest (RF) modeling to capture nonlinear interactions. Results indicate weak but statistically significant correlations between AOD and PM2.5 (r ≈ 0.26-0.30), with stronger monotonic relationships. A pronounced seasonal dependence is observed, with the strongest coupling in autumn and weak or insignificant relationships in winter. Partial correlation analysis suggests that a residual association between AOD and PM2.5 remains after accounting for meteorological influences.

RF models improve predictive performance (R² ≈ 0.39), although performance degrades in winter. Sensitivity analysis indicates that transported smoke plumes can influence the AOD-PM2.5 relationship, particularly when partial mixing into the boundary layer occurs.

Over the past decade, nearly ten satellites dedicated to atmospheric CO2 concentration monitoring have been launched, significantly advancing our understanding of the global carbon cycle. In 2022, China launched the DaQi-1 (DQ-1) satellite, which carries the Aerosol and Carbon Dioxide Lidar (ACDL)—the first spaceborne lidar sensor for CO2 monitoring. Relying on laser-based active sensing, ACDL can detect global XCO2 at nighttime, serving as an important complement to existing passive optical CO2 satellite missions. This study aims to introduce the scientific community to the XCO2 retrieval methodology of ACDL and its initial XCO2 product. The first version of ACDL XCO2 products scheduled for release is called “v1.0”. This paper presents a comparison between XCO2 at daytime and nighttime. Nonetheless, challenges remain, including reliance on meteorological reanalysis data and uncertainties in spectroscopic parameters. In future product versions, we plan to improve data quality through enhanced denoising techniques and signal processing methods for low signal-to-noise ratio (SNR) cases. We hope that this initial ACDL XCO2 product will spark broader interest and participation from the scientific community, thereby contributing fresh momentum to climate change research.

Traditional forecasting methods of air pollutants show intrinsic limitations due to the complexity of atmospheric interactions. Recent research has moved toward the employment of artificial intelligence (AI)-based approaches and satellite data processing. The framework proposed in this study is a transfer learning (TL) model to estimate surface-level NO2 concentrations across multiple locations by using satellite and environmental data.

The approach integrates Sentinel-5P TROPOMI-derived tropospheric NO2 columns, meteorological variables (temperature, precipitation etc), spatial coordinates and temporal features.

A CatBoost regression model is implemented, leveraging a Leave-One-City-Out (LOCO) TL framework across five cities (Berlin, London, Madrid, Paris and Toronto) in the world. This enables the model transfer from multiple source domains to a new target city with minimal ground-based data. Experimental results are outperforming city-specific baseline models, by showing an increased prediction accuracy, a reduced Root Mean Square Error (RMSE) by approximately 7% and a Coefficient of Determination (R2) higher by 2.7%. Toronto, which represents an environment with a low monitoring density, benefits most from TL, with R2 improving from 0.58 (baseline) to 0.66 (transfer) and RMSE dropping from 6.44 µg/m3 to 5.84 µg/m3.

A detailed Leave-One-Block-Out (LOBO) ablation study shows how each group of features contributes to the performance of the model. Spatial coordinates and meteorological features are the most influential predictors of NO2 concentration, while the satellite NO2 data increase model generalization. These results highlight the potential of cross-city TL and remote sensing synergy for scalable urban air pollution monitoring, especially in limited ground-based monitoring scenarios.

High-resolution mapping of tropospheric ozone is essential for urban environmental assessment; however, satellite-derived ozone products are generally too coarse to capture neighborhood-scale variability in complex megacities such as Tehran. This study introduces an interpretable deep-learning framework that downscales coarse Sentinel-5P ozone observations to a 30-m spatial grid by integrating a U-Net convolutional architecture with SHapley Additive exPlanations (SHAP). A diverse suite of predictors—including land-surface indicators, meteorological parameters, terrain morphology, and chemical precursors—was harmonized and resampled to a unified spatial resolution. SHAP analysis was applied to quantify each predictor’s contribution, enabling the removal of redundant or low-impact variables before model training. Using spring 2020 as the evaluation period, the optimized U-Net successfully reconstructed fine-scale ozone gradients and reproduced Tehran’s characteristic north–south pattern driven by topography and emission density. Comparative analysis with preliminary outputs demonstrates that feature optimization enhances spatial coherence, reduces noise artifacts, and improves the representation of localized hotspots. Statistical evaluation further showed strong agreement between the downscaled ozone estimates and observational data at both station and district scales, demonstrating effective generalization across heterogeneous urban environments. Overall, the findings highlight the potential of combining deep learning with interpretability techniques to refine coarse satellite ozone observations and provide a scalable, high-resolution framework for urban air-quality monitoring and exposure assessment.