Considering that our key results emphasize the persistence over time of disparities (especially relative disparities) -rather than absolute concentrations at specific locations -our overarching qualitative insights are likely to be robust. Relative disparities are principally determined by the interaction of fine-scale spatial patterns of demographics, roadways, and fleet activity, and are less sensitive to the magnitude of emissions or concentrations. In the supplementary materials (supplementary text section "Model Uncertainty and Sensitivity" and Figs. S16-S17), we explore how possible biases in emissions estimates and model performance could affect our results. We first review the literature to constrain our understanding of the uncertainty from the state regulatory model of on-road mobile source emissions estimates . From previous work validating EMFAC's on-road mobile source emission factors and a similar model's activity-based spatial surrogates, we believe our qualitative insight is unlikely to be affected by bias in the emissions inventory. We then perform four tests to evaluate the sensitivity of our analyses to potential biases in the emissions and model (Fig. ). A key insight is that within the range of expected model biases for InMAP, we find that the magnitude and ordering of relative disparities is only minimally sensitive. This result arises in part because relative disparities involve ratios of modeled concentration estimates. In addition, because we find meaningful disparities for each of the five modeled PM2.5 constituents, pollutant-specific model biases (representing, for example, a possible mischaracterization of the non-linear chemistry in InMAP) are unlikely to strongly skew our results (see Fig. ). Likewise, this analysis implies that inventory biases that affect aggregate level of emissions are unlikely to affect our core insights. We show that our results are robust against spatial biases in the emissions inventory by repeating our analysis with emissions from two independently derived, peer-reviewed emissions inventories and coarser representations of our emissions inventory. Spatial emissions biases in the inventory could conceivably affect conclusions about relative disparities if they were much larger than what we explored in Fig. . However, we consider this implausible given how closely our results align with disparity insights from highresolution NO2 predictions (Fig. ). Nonetheless, because neither EMFAC nor InMAP are meant to authoritatively describe emissions and concentrations in individual model pixels, we ascribe our greatest confidence to overall patterns in space and time, but caution against overinterpreting results for specific communities or other small regions. Finally, in a sensitivity analysis (Fig. ), we repeated our analysis with 2000 Decennial Census data. These analyses indicate that our core qualitative finding of highly persistent relative disparity was generally not sensitive to the selection of census dataset. In particular, the choice of which demographic dataset is used minimally affected the PWM concentrations and relative disparities experienced by overburdened communities and individual racial-ethnic groups in California. However, future demographic shifts in the California population that substantially alter patterns of social segregation could meaningfully affect aggregate air pollution disparities at the state level. For example, if suburbs become more racially integrated and California's population becomes increasingly diverse, relative disparities could decrease as a function of demographic changes and not necessarily emissions mitigation.