Access to Interpretable Data to Support Disproportionate Health Risks from Industrial Releases: A Case Study on the Environmental Protection Agency’s Datasets and Their Application to the Latinx Communities of Houston, Texas
Bibliographic record
Abstract
Latinx communities face disproportionate environmental injustices and are targeted due to systematic economic and political inequities. This research evaluates the ease at which links between industrial releases and risk of adverse health effects can be defined to influence policy change in Houston, TX. The Environmental Protection Agency (EPA)'s Toxic Release Inventory (TRI) is the most comprehensive public database on industrial facilities' toxic chemical releases in the US. TRI is presented within a risk-based context through the Risk Screening Environmental Indicators (RSEI) scores. TRI and RSEI datasets for Houston in 2022 were assessed in QGIS to analyze chemical release and risk in neighborhoods using Community Tabulation Areas (CTAs), identifying demographics of communities facing disproportionate industrial releases and consequent potential health risks. Geospatial visualizations reflected Latinx communities to house the heaviest polluting industrial facilities in Houston. As a result, these communities face the highest potential risk of adverse health effects due to exposure to a multitude of chemicals-particularly 1,3-butadiene, benzene, and chromium-as reflected in cumulative RSEI scores. An analysis of TRI and RSEI datasets elucidates the burden of gathering and analyzing chemical release data in a public health context, reflecting why change beginning at the local level can be difficult for under-resourced Latinx communities facing industrial pollution. Improving the accessibility and utility of the EPA resources will provide a resource to advocate for data-driven policy change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".