Fusing Spatial Kriging with Satellite Estimates to Obtain a Regional Estimation of PM2.5

This work focuses on developing estimates of ground-level fine particulate matter (PM2.5) in the northeastern U.S. based on measurements derived from the Air Quality System (AQS) repository. Real time monitoring of PM2.5 is important due to its effect on climate change and human health, however, designated samplers used by state agencies do not provide optimal spatial coverage given their high cost and extensive human labor dependence. Through the application of remote sensing instruments, information about PM2.5 concentrations can be generated at certain locations. On the other hand, coverage limitation also occurs when using satellite remote sensing methods due to atmospheric conditions. Therefore, our approach begins by utilizing surface PM2.5 measurements collected from the Remote Sensing Information Gateway (RSIG) portal in order to build fine particulate matter estimations by applying a Spatial Kriging technique. Then, we combine our Kriging estimations to the satellite derived PM2.5 obtained through an Artificial Neural Network (ANN) scheme to generate a daily regional PM2.5 product. Finally, evaluation of our fused algorithm's technique is assessed by performing comparisons against Kriging and neural network individual performances, showing the promising value added by the combination of these two techniques in producing more accurate estimations of surface level PM2.5 over our region of interest.