High-Resolution PM$_{10}$ Estimation Using Satellite Data and Model-Agnostic Meta-Learning

Characterizing the spatial distribution of particles smaller than 10 μm (PM$_{10}$) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM$_{10}$ at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM$_{10}$ concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM$_{10}$, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM$_{10}$ in Beijing, with coefficient of determination of 0.75. MAML improves the PM$_{10}$ estimation performance of the ANN model compared with the baseline using pre-trained initial weights. ... mehrThus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.