Aerosol Optical Depth

Aerosol optical depth (AOD) is a measure of how much aerosols in the atmosphere reduce the amount of sunlight reaching the Earth's surface, a key indicator of air quality. Current research heavily focuses on improving AOD retrieval and its use in estimating PM2.5 concentrations, employing advanced machine learning techniques such as deep learning (including convolutional neural networks and deep ensemble forests) and Gaussian processes to overcome limitations like cloud cover and enhance spatial resolution. These advancements enable more accurate high-resolution mapping of PM2.5 pollution, improving air quality monitoring and informing public health interventions. Data fusion strategies combining multiple satellite sensors and algorithms are also being explored to further refine AOD-based PM2.5 estimations.