Authors: Manzhu Yu*, Pennsylvania State University, Qian Liu, George Mason University
Topics: Environment, Geographic Information Science and Systems, Remote Sensing
Keywords: Nitrogen dioxide, spatial downscaling, spatial interpolation, deep learning
Session Type: Virtual Paper
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 18
Presentation File: No File Uploaded
Air quality is one of the major issues within an urban area that affect people’s living environment and health conditions. Existing observations are not adequate to provide spatiotemporally comprehensive air quality information for vulnerable populations to plan ahead. Launched in 2017, TROPOMI provides a high spatial resolution (~5km) tropospheric air quality measurement that captures the spatial variability of air pollution, but still limited by its daily overpass in the temporal dimension and relatively short historical records. Integrating with the hourly available AirNOW observations by ground-level discrete stations, we proposed and compared two deep learning methods that learn the relationship between the ground-level nitrogen dioxide (NO2) observation from AirNOW and the tropospheric NO2 column density from TROPOMI to downscale the daily NO2 to an hourly resolution. The learned relationship can be used to produce NO2 emission estimates at the sub-urban scale on an hourly basis. The two methods include 1) an integrated method between inverse weighted distance and a deep neural network (IDW+DNN), and 2) a deep matrix neural network that maps the discrete AirNOW observations directly to the distribution of TROPOMI observations. We further compared the accuracies of both models using different configurations of input predictors and validated their average Root Mean Squared Error (RMSE), average Mean Absolute Error (MAE), and the spatial distribution of errors. Results show that the deep matrix model generates more reliable NO2 estimates and captures a better spatial distribution of NO2 concentrations than the IDW+DNN model.