A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the
respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air
quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial
domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the
atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating
PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the
concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature,
and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as
AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High
Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS.

The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies
among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they
intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to
traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations.
Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the
estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for
multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are
created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models
are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble
training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and
related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically
different between the merged and unmerged models are expected to improve overall performance. These new parameters are
then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5
providing the best results.