Air pollution is a plausible risk factor for autoimmune disease development. Other inhalants such as tobacco smoke and silica are strongly associated with the development of RA, related to their ability to directly interact with alveolar tissue [4, 5]. Air pollution has been demonstrated to be able to directly stimulate an inflammatory response  and indirectly alter the microbiome . A relationship between particulate matter exposure and elevations in inflammatory marker levels has been described [8–11]. As randomized controlled trials to assess directly for causation between air pollutant exposures and disease development in humans are not feasible, we must rely on observational studies to assess for evidence of associations. Fortunately, several methods to estimate air pollutant exposure exist. A variety of air pollutants from industrial and private sources are measurable at fixed-site continuous monitoring stations that collect hourly mean levels of criteria air pollutants, including particulate matter <2.5 μm in size (PM2.5), particulate matter <10 μm in size (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). The hourly data can then be averaged to obtain defined temporal estimates for the region (e.g., a city). Land use regression models use Geographic Information System (GIS) to estimate air pollutant exposure through a combination of land use, traffic, population density, physical geography, and meteorology across an area  and predict concentrations at a defined site, such as an individual's location of residence determined by postal code . Inverse distance weighting (IDW) uses the general principle of interpolation, where values at a site are estimated based on distance from a measured value at another point (e.g., a monitoring station) under the presumption of reducing pollutant levels with increasing distance . Finally, remote sensing through satellite imaging yields estimates across broad geographic areas.