Remote Sensing Applications for Improved Estimation of Particulate Matter in California with Implications for Public Health

Particulate matter less than 2.5 microns (PM2.5) is emitted into the atmosphere through human processes (mechanically and chemically) and climate/natural events (wind driven emissions such as dust and sea spray, volcanic eruptions, and forest fires). These particles enter the lungs though gas exchanging organs and later enter the bloodstream making PM2.5 an important public health concern. Recent studies have focused on modelling PM2.5 using remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data. These models vary depending on the geographic region and have been weak in the Western United States. This study improves these PM2.5 - AOD regression models in three California cities through the incorporation of meteorological variables (temperature, relative humidity, precipitation, peak gust speed, and wind direction). Moreover, applying AOD data from the Dark Target collection six retrieval algorithm at a 3 km spatial resolution results in models even more robust than those developed in the Midwest. Our study also reestablishes the connection between PM2.5 and public health concerns including respiratory and cardiovascular diseases.