Estimating Monthly PM2.5 Concentrations Across the Chicagoland Area from 2014-2018 Using High Resolution Aerosol Optical Depth Data and a Three-Stage Neural Network Model

Abstract:

Measurements of Aerosol Optical Depth (AOD) from NASA’s Terra satellite provide a remote sensing solution to pollution monitoring worldwide. Specically, AOD allows researchers to proxy PM2.5 concentrations after controlling for a variety of spatial and temporal predictors. The MAIAC algorithm uses advanced image processing techniques to improve the spatial resolution of the Terra AOD product from 10km to 1km, allowing for high resolution PM2.5 modeling. For large metropolitan areas, incorporating a hybrid-sensor approach may provide an alternative for pollution surveillance to using sparse ground sensors alone.

We implement a three-stage neural network model to predict monthly PM2.5 concentrations in the Chicagoland area (across 21 counties) between 2014 and 2018. Spatial predictors include population density, developed land cover, and elevation. Temporal predictors in the model consisted of a variety of meteorological variables (temperature, pressure, wind velocity, wind direction, and visibility), as well as NDVI and a point source emissions inventory. Dummy variables for each month were also included to account for seasonal variation in temporal data. All data wrangling and model output results were opened using R in a public repository.

The model achieved an out-of-sample R-squared of 0.59, and was able to locate high pollution areas like major airports, expressways, and some industrial areas. Pollution levels decreased from 2014 to 2017 before increasing in 2018, with peak PM2.5 levels in late summer. While performance was not as high as models covering larger regions, it generated more detail than publicly available data resources. By open sourcing the data preparation and results, the model can continue to improve and be rened over time. High spatial resolution data has the potential to provide regional policymakers and communities with better insights on the relationship between PM2.5 pollution and health outcomes.