DOI: 10.5061/dryad.t76hdr87t
title: Data from: PM2.5 exposure disparities persist despite strict vehicle emissions controls in California

description: As policymakers increasingly focus on environmental justice, a key
 question is whether emissions reductions aimed at addressing air quality
 or climate change can also ameliorate persistent air pollution exposure
 disparities. We examine evidence from California’s aggressive vehicle
 emissions control policy from 2000 to 2019. We find a 65% reduction in
 modeled statewide average exposure to PM2.5 from on-road vehicles, yet for
 people of color and overburdened community residents, relative exposure
 disparities increased. Light-duty vehicle emissions are the main driver of
 the exposure and exposure disparity, although smaller contributions from
 heavy-duty vehicles especially affect some overburdened groups. Our
 findings suggest that a continued trend of emissions reductions will
 likely reduce concentrations and absolute disparity but may not reduce
 relative disparities without greater attention to the systemic factors
 leading to this disparity.
description: # Data to support: PM2.5 exposure disparities persist despite strict
 vehicle emissions controls in California
 [https://doi.org/10.5061/dryad.t76hdr87t](https://doi.org/10.5061/dryad.t76hdr87t) ## Description of the data and file structure For this work, we developed and used an open-source Python-based pipeline that streamlines exposure concentration and health impact analyses called Estimating Concentrations and Health Outcomes – Automated ISRM Resource ([ECHO-AIR](https://echo-air-model.github.io/)). The files contained in this repository are organized into two sections: Output Files from ECHO-AIR and Supporting Jurisdictional Files. ### ECHO-AIR Output Files We have provided two different concentration outputs from ECHO-AIR for 2000 through 2019 and for four fleets (all, LDV, MDV, and HDV). #### Detailed Concentration The detailed concentration outputs are uploaded as shapefiles with the following title scheme: '[**fleet**]_cy[**year**]_detailed_concentration.shp'. The detailed concentration outputs also include the emissions used for this analysis. We obtained estimates of mobile emissions in California from CARB’s EMission FACtor (EMFAC) model (version EMFAC 2021 with MPOv11) for calendar years 2000 through 2019 (31). Estimated emissions were spatially allocated to a 1 km by 1 km grid using surrogates from the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System and ARB’s Emission s Spatial and Temporal Allocator (ESTA) models. The emissions data uploaded here have been aggregated to the California ISRM grid cell. These files have the following columns: * **ISRM_ID**: index for matching with [the California ISRM](https://zenodo.org/records/7548607) * **PM25_UG/S**: total emissions of PM2.5 in ug/s * **NH3_UG/S**: total emissions of NH3 in ug/s * **VOC_UG/S**: total emissions of VOC in ug/s * **NOX_UG/S**: total emissions of NOx in ug/s * **SOX_UG/S**: total emissions of SOx in ug/s * **fPM_UG_M3**: the concentration of primary PM2.5 in ug/m3 * **fNH3_UG_M3**: the concentration of ammonium PM2.5 in ug/m3 * **VOC_UG_M3**: the concentration of organic PM2.5 in ug/m3 * **fNOX_UG_M3**: the concentration of nitrate PM2.5 in ug/m3 * **fSOX_UG_M3**: the concentration of sulfate PM2.5 in ug/m3 * **PM25_UG_M3**: the concentration of total PM2.5 * **LAYER**: the release height layer of the ISRM #### Exposure Concentration The exposure concentration outputs are uploaded as shapefiles with the following title scheme: '[**fleet**]_cy[**year**]_exposure_concentrations.shp'. These files have the following columns: * **ISRM_ID**: index for matching with [the California ISRM](https://zenodo.org/records/7548607) * **PM25_UG_M3**: the concentration of total PM2.5 * **TOTAL**: the total population allocated to the grid cell * **ASIAN**: the Asian (Non-Hispanic) population allocated to the grid cell * **BLACK**: the Black (Non-Hispanic) population allocated to the grid cell * **HISLA**: the Hispanic (all races) population allocated to the grid cell * **INDIG**: the Native American (Non-Hispanic) population allocated to the grid cell * **PACIS**: the Pacific Islander (Non-Hispanic) population allocated to the grid cell * **WHITE**: the White (Non-Hispanic) population allocated to the grid cell * **OTHER**: the Other (Non-Hispanic) population allocated to the grid cell ### Jurisdictional Boundaries We have also included two useful shapefiles for defining the boundaries for overburdened communities. 1. **ab617_communities.shp**: shapefile defining the boundaries of the 19 AB617 communities 2. **SB535_tracts.shp**: the shapefile defining the boundaries of the SB535 Disadvantaged Communities ## Code/Software Code used to process this data can be found on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.11069005). The full details of the ECHO-AIR model can be found on the [ECHO-AIR website](https://echo-air-model.github.io/).