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Record W4416983234 · doi:10.1016/j.heliyon.2025.e44289

Air pollution and environmental justice: a systematic literature review on methodological approaches

2025· article· en· W4416983234 on OpenAlexaboutno aff
Alexandra Monteiro, Elisabete Figueiredo, Vera Rodrigues, C. Cardoso, Myriam Lopes, Peter Roebeling, Hélder Relvas, Peter Seixas, Sónia Gouveia, Anne Elise Gomes, Ana Martins, Carla Gama, Ivana Picone Borges de Aragão, Ana Isabel Miranda, Enda Hayes

Bibliographic record

VenueHeliyon · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental justiceAir pollutionSystematic reviewProxy (statistics)PopulationHuman healthChinaSystematic errorAir quality index

Abstract

fetched live from OpenAlex

<h2>Abstract</h2> Air pollution is the greatest environmental risk to human health, according to the WHO and other international entities like the UN Human Rights Council and EEA. However, not everyone is equally affected by air pollution, depending on exposure and vulnerability. The objective of this systematic literature review is to investigate whether and how concepts related to environmental inequalities or justice have been considered when assessing health impacts of air pollution, identify possible gaps in knowledge, and offer suggestions for future research. A total of 99 articles are assessed considering framework, study region, time scale, data and indicators used and finally, the method applied (statistical, numerical or survey). Results show that EJ studies took place in 21 different countries, led by the United States of America (53 %), followed by Canada (6 %) and China (5 %). In terms of temporal coverage, more than 50 % of the studies were published in the last 5 years and are mainly focused on long-term studies. As regards proxy data, most of the studies focused on PM2.5, but only 50 % explicitly include health or exposure data. All studies evaluated EJ questions related to socio-economic status (SES) or race/ethnicity. The socio-economic indicators mostly used are associated with income (99 %), followed by population characterization and housing. The most common method used was statistical analysis (71 %), with 14 % applying surveys and 15 % using modelling for scenario approaches. These results point out that there is space for other case studies in Europe among other regions of the world, with more robust statistical models based on advanced mixed methods to deal with such multivariable research. At small geographic units, studies supported by survey information would be recommended to include both individual and contextual socio-economic indicators. A limited number of studies deal with social perceptions and citizen engagement. Future research is suggested. It should be noticed that this literature review only looks for occurrences of "environmental justice" related to air pollution/quality.

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)0.001
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.118
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.100
GPT teacher head0.365
Teacher spread0.265 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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