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Record W4386116116 · doi:10.1289/ehp11344

Modeling and Analysis of Air Pollution and Environmental Justice: The Case for North Carolina’s Hog Concentrated Animal Feeding Operations

2023· article· en· W4386116116 on OpenAlexfundno aff
Brandon M. Lewis, William Battye, Viney P. Aneja, Honghyok Kim, Michelle L. Bell

Bibliographic record

VenueEnvironmental Health Perspectives · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Environmental Health SciencesWellcome TrustHealth CanadaNorth Carolina State UniversityNational Institutes of HealthU.S. Environmental Protection Agency
KeywordsEnvironmental justiceAERMODAir pollutionEnvironmental healthEnvironmental scienceGeographyEnvironmental protectionMedicineEcologyBiologyAtmospheric dispersion modeling

Abstract

fetched live from OpenAlex

Background: Concentrated animal feeding operations (CAFOs) emit pollutants that can cause negative impacts on human health. The concentration of hog production in North Carolina raises concerns regarding the disproportionate exposure of vulnerable communities to air pollution from CAFOs. Objectives: We investigated whether exposure to gaseous ammonia (NH3) and hydrogen sulfide (H2S) (in 2019) differs between subpopulations by examining demographics, including race/ethnicity, age, educational attainment, language proficiency, and socioeconomic status. Methods: We used an Air Monitoring Station (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD)–based Human Exposure Model (version 3) to estimate ambient concentrations of NH3 and H2S from hog farms in Duplin County and its surrounding counties in North Carolina and estimate subsequent exposures of communities within 50km of Duplin County, North Carolina, or the Duplin County Region. We combined estimated exposures with 2016 American Community Summary Census data, at the block group level, using spatial analysis to investigate whether exposures to these pollutants differ by race and ethnicity, age, income, education, and language proficiency. Based on these estimations, we assessed associated exposure risks to the impacted communities and used multivariable regression modeling to evaluate the relationship between average ammonia exposures from Duplin regional hog farms and the presence of vulnerable populations. Results: The average [±standard deviation (SD)] annual estimated concentration of NH3 and H2S in the Duplin County Region is 1.75±2.81 μg/m3 and 0.0087±0.014 μg/m3, respectively. The maximum average annual ambient concentrations are estimated at 54.27±4.12 μg/m3 and 0.54±0.041 μg/m3 for NH3 and H2S, respectively. Our descriptive analysis reveals that people of low income, people of color, people with low educational attainment, and the linguistically isolated in the Duplin Region are disproportionately exposed to higher levels of pollutants than the average exposure for residents. Alternatively, our statistical results suggests that after adjusting for covariates, communities of color are associated with 1.70% (95% CI: −3.79, 0.44) lower NH3 concentrations per 1-SD increase. One-standard deviation increases in the adults with low educational attainment and children <19 years of age is associated with 1.26% (95% CI: −0.77, 3.33) and 1.20% (95% CI: −0.62, 3.05) higher NH3 exposure per 1-SD increase, respectively. Discussion: Exposures to NH3 and H2S differed by race and ethnicity, educational attainment, language proficiency, and socioeconomic status. The observed associations between exposure to CAFO-generated pollutants and sociodemographic indicators differed among demographics. The disproportionate distribution of hog facilities and resulting pollutant exposures among communities may have adverse environmental and human health impacts, raising environmental justice concerns. https://doi.org/10.1289/EHP11344

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.283
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2023
Admission routes1
Has abstractyes

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