COMPARISON OF SPATIAL INTERPOLATION METHODS FOR BETTER AIR QUALITY ESTIMATION

Recent advances in geospatial technologies and earth data opened up a wide array of aerosol science and allow detailed assessment of air quality. Globally, wildfire-driven air pollution is often cited as one of the significant reasons for adverse impacts on public health. In recent times, ambient concentrations of PM2.5 in the US have experienced a remarkable degradation due to wildfire smoke, which has jeopardized the country’s efforts for the last few decades to improve air quality. Since 2016, the effects have been more alarming in western states like California, Oregon, and Washington, where smoke has added enough pollution to the air to wipe out nearly half of the total air quality gains made from 2000 onward. United States Environmental Protection Agency (USEPA) has regularly monitored levels of several ambient air pollutants including PM2.5 across the country since the 1970s. However, the lack of monitoring stations often makes it critical to estimate the subjects’ exposure to those criteria air pollutants. The spatial interpolation technique is one of the effective methods to estimate air quality, though there are differences in spatial and temporal distributions. This paper aims to examine method-specific differences in estimated concentration levels and determine conditions under which different methods produce significantly different concentration values. We employed Inverse distance weighting (IDW) and Spline interpolation methods and found that different interpolation methods produce substantially different estimations in California. Our results suggest that both models are overpredicting though Spline interpolation produces comparatively better results than IDW. The outcome of this project will help us provide insights into the use of EPA monitoring data with better spatial interpolation methods.