Given the serious
adverse health effects associated with many pollutants,
and the inequitable distribution of these effects between socioeconomic
groups, air pollution is often a focus of environmental justice (EJ)
research. However, EJ analyses that aim to illuminate whether and
how air pollution hazards are inequitably distributed may present
a unique set of requirements for estimating pollutant concentrations
compared to other air quality applications. Here, we perform a scoping
review of the range of data analytic and modeling methods applied
in past studies of air pollution and environmental injustice and develop
a guidance framework for selecting between them given the purpose
of analysis, users, and resources available. We include proxy, monitor-based,
statistical, and process-based methods. Upon critically synthesizing
the literature, we identify four main dimensions to inform method
selection: accuracy, interpretability, spatiotemporal features of
the method, and usability of the method. We illustrate the guidance
framework with case studies from the literature. Future research in
this area includes an exploration of increasing data availability,
advanced statistical methods, and the importance of science-based
policy.