  Understanding the key factors shaping environmental hazard exposures and
their associated environmental injustice issues is vital for formulating
equitable policy measures. Traditional perspectives on environmental injustice
have primarily focused on the socioeconomic dimensions, often overlooking the
influence of heterogeneous urban characteristics. This limited view may
obstruct a comprehensive understanding of the complex nature of environmental
justice and its relationship with urban design features. To address this gap,
this study creates an interpretable machine learning model to examine the
effects of various urban features and their non-linear interactions to the
exposure disparities of three primary hazards: air pollution, urban heat, and
flooding. The analysis trains and tests models with data from six metropolitan
counties in the United States using Random Forest and XGBoost. The performance
is used to measure the extent to which variations of urban features shape
disparities in environmental hazard levels. In addition, the analysis of
feature importance reveals features related to social-demographic
characteristics as the most prominent urban features that shape hazard extent.
Features related to infrastructure distribution and land cover are relatively
important for urban heat and air pollution exposure respectively. Moreover, we
evaluate the models' transferability across different regions and hazards. The
results highlight limited transferability, underscoring the intricate
differences among hazards and regions and the way in which urban features shape
hazard exposures. The insights gleaned from this study offer fresh perspectives
on the relationship among urban features and their interplay with environmental
hazard exposure disparities, informing the development of more integrated urban
design policies to enhance social equity and environmental injustice issues.
