Physics-Based Spatial Oversampling of TROPOMI NO₂ Observations to US Neighborhoods Reveals the Disparities of Air Pollution

Satellite observations provide continuous and global coverage observations of air pollutants, widely used to inform health impacts and air pollution disparities. Linking satellite retrievals with socioeconomic or health data involves matching the irregularly shaped satellite observations with administrative units. Here, we develop a physics-based approach to spatially oversample nitrogen dioxide (NO₂) retrievals from TROPOspheric Monitoring Instrument (TROPOMI) directly to United States (US) neighborhoods (i.e., block groups). The physics-based oversampling approach considers each satellite pixel as a sensitivity distribution, meaning that satellite instruments are more sensitive to the neighborhoods at the center than at the edge of the observations. We show that directly oversampling satellite observations to administrative shapes is a more accurate and computationally efficient approach than the commonly used gridding approaches, and it is advantageous for shorter temporal windows. Combining the newly developed NO₂ data set with demographic data, we find widespread racial/ethnic and income-related NO₂ disparities across the US. NO₂ disparities are even more pronounced during the most polluted days, suggesting greater acute health effects for overburdened communities. We expect that the resolution-adaptive, neighborhood-level, and GIS-compatible NO₂ data set would lower barriers of the public to access and interpret satellite observations, facilitating the actionable applications of satellite observations.