# Methodological Design Choices Can Affect Air Pollution Exposure Disparity Estimates: A Case Study on California’s Agricultural Sector

## TL;DR

This study shows how different methodological choices in measuring air pollution exposure can lead to different conclusions about racial and ethnic disparities in California's agricultural sector.

## Contribution

The study demonstrates how methodological design choices affect exposure disparity estimates in air pollution research.

## Key findings

- Methodological choices like exposure input and study geography influence disparity estimates.
- Disparities at the mean and extremes of exposure distributions can differ significantly.
- Study design decisions impact conclusions and mitigation strategies for pollution exposure.

## Abstract

People
of color in the United States are disproportionately and
unfairly exposed to air pollution. Equity-oriented scientific evaluations
quantifying these disparities often use population-average exposure
metrics to capture the overall inequality within a system. Utilizing
these metrics involves choices about the exposure input for assessing
disparity, the study geography, and the reference population, which
are critical to understanding disparities and effectively designing
interventions. Here, we use a case study of exposure to fine particulate
matter (PM2.5) from California’s agricultural sector
to dissect the implications of these decisions. Using a reduced-complexity
model and emissions of PM2.5 and precursors, we compare
estimates of racial and ethnic disparities in exposure resulting from
different combinations of these methodological choices. The full population
distributions highlight differences between disparities at the extremes
(e.g., 90th percentile) and at the mean. Additionally, the selection
of study geography and reference population can influence the magnitude
and relative ordering of exposure disparities. Thus, methodological
choices can lead to different conclusions for the same concentration
and population surfaces; this can impact not only the findings of
an individual study but also have implications for mitigation strategies.
We conclude with recommendations for best practices for making, justifying,
and communicating these methodological decisions.