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<p>Frequent and widespread wildfires in the northwestern United States and Canada have become the “new normal” during the Northern Hemisphere summer months, which significantly degrades particulate matter air quality in the United States. Using the mid-visible Multi Angle Implementation of Atmospheric Correction (MAIAC) satellite-derived aerosol optical depth (AOD) with meteorological information from the European Centre for Medium-Range Weather Forecasts (ECMWF) and other ancillary data, we quantify the impact of these fires on fine particulate matter concentration (PM<span class="inline-formula">_2.5</span>) air quality in the United States. We use a geographically weighted regression (GWR) method to estimate surface PM<span class="inline-formula">_2.5</span> in the United States between low (2011) and high (2018) fire activity years. Our results indicate an overall leave-one-out cross-validation (LOOCV) <span class="inline-formula"><i>R</i>²</span> value of 0.797 with root mean square error (RMSE) between 3 and 5 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>. Our results indicate that smoke aerosols caused significant pollution changes over half of the United States. We estimate that nearly 29 states have increased PM<span class="inline-formula">_2.5</span> during the fire-active year and that 15 of these states have PM<span class="inline-formula">_2.5</span> concentrations more than 2 times that of the inactive year. Furthermore, these fires increased the daily mean surface PM<span class="inline-formula">_2.5</span> concentrations in Washington and Oregon by 38 to 259 <span class="inline-formula">µ</span>g m<span class="inline-formula">⁻³</span>, posing significant health risks especially to vulnerable populations. Our results also show that the GWR model can be successfully applied to PM<span class="inline-formula">_2.5</span> estimations from wildfires, thereby providing useful information for various applications such as public health assessment.</p>