A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.jaerosci.2020.105704. This is version 3 of this Preprint.

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.


Download Preprint

Supplementary Files

Khanh Do, Haofei Yu, Jasmin Velasquez, Marilyn Grell-Brisk, Heather Smith, Cesunica Ivey 


This study elucidates PM2.5 exposure disparities in a socioeconomically diverse air basin that is heavily burdened by air pollution. A novel spatial clustering approach is applied to classify the microenvironments of more than 900,000 high temporal resolution personal exposure data points. Results from the study indicate that participants from the lowest socioeconomic status community experienced overall higher personal exposures over consecutive 24-hr monitoring periods, despite high participant mobility and low variability in ambient PM2.5 during the study. Our inclusive monitoring protocol minimizes participant fatigue and is well-suited for real-time, long-term characterization of PM2.5 exposure disparities in underserved communities.




Civil and Environmental Engineering, Engineering, Environmental Engineering


air pollution, environmental exposure, GIS, personal exposure


Published: 2020-08-16 13:51

Last Updated: 2023-11-28 09:32

Older Versions

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
All data are presented in aggregated form in accordance with UC Riverside Institutional Review Board guidelines. Individual datasets cannot be distributed in order to preserve the privacy of participants.