Evidence map›Paper›PMID 41068655›Full record

ArticleBMC public health2025

A tale of two cities: contrasting equity of greenspace benefits in relation to PM

Katherine Laura Farley, Danial Owen, Alice Fitch, David Fletcher, Janice Scheffler, Laurence Jones

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Katherine Laura FarleyUK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, LL57 2UW, UK. katfar@ceh.ac.uk.ORCID http://orcid.org/0000-0001-6993-9065
Danial OwenUK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, LL57 2UW, UK.ORCID http://orcid.org/0000-0001-7744-9701
Alice FitchUK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, LL57 2UW, UK.ORCID http://orcid.org/0000-0002-6260-8957
David FletcherUK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, LL57 2UW, UK.ORCID http://orcid.org/0000-0001-5029-7453
Janice SchefflerUK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, UK.ORCID http://orcid.org/0000-0002-7398-4237
Laurence JonesUK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, LL57 2UW, UK.ORCID http://orcid.org/0000-0002-4379-9006

Funding

Horizon 2020 821016NERC Defrag Project NE/W002892/1UK RECLAIM Network Plus EP/W034034/1
6 · The paper itself

Abstract

backgroundUrban air pollution is the second highest risk factor for non-communicable diseases. Socioeconomically deprived populations are at greater risk from related adverse health outcomes and often bear the additional burden of living in areas with higher pollution exposure. To reduce this inequality policy makers need to understand the relationship between the location of interventions and improvements in air quality. The 3–30-300 greening rule outlines guidance to improve residents’ access to trees and greenspace. No studies have yet evaluated its implications for reductions in inequalities or co-benefits beyond greenspace access. This paper explores the relationship between exposure to PM2.5 and socioeconomic status in Paris and Aarhus, and the potential impact of the 3–30-300 rule on health inequalities.

methodWe use air quality models and socioeconomic data to calculate disparities in access to greenspace and exposure to fine particulate air pollution across subgroups using two land cover scenarios: existing greenspace conditions and potential enhancements following the 3–30 rule. The following socioeconomic data were disaggregated to residential buildings: households in poverty, age group, citizenship, employment, and education. Exposure to air pollution was calculated as the population-weighted average of PM2.5 concentration (µg m−3).

resultsThe relationship between socioeconomic characteristics and exposure to poor air quality differed in each city: In Paris, populations often considered more vulnerable to adverse health outcomes (unemployed, non-citizens, lower education level, lowest income) were exposed to higher concentrations of PM2.5, (although the relationship with income was nonlinear), whilst both older and younger populations had slightly lower particulate exposure than working age populations. By contrast, in Aarhus, populations with higher socioeconomic status (and by association, lower vulnerability) in terms of income, education, citizenship and employment status, were exposed to higher concentrations of PM2.5, whilst children were the age group least exposed to fine particulates. Increasing greenspace under the 3–30 had contrasting effects on inequities in each city: mitigations improved air quality for all populations in both but, in Paris, the greatest benefit was experienced by those with lower baseline exposure as well as lower vulnerability. In Aarhus larger air quality improvements were experienced by populations with the highest baseline exposure except for the income characteristic. These were not groups expected to be at greater risk of adverse health outcomes.

conclusionsReductions in PM2.5 concentration can be produced via targeted increases in trees. The impact on public health outcomes depends in part, on the spatial distribution of socioeconomic groups. Somewhat counter-intuitively, the 3–30 rule in both cities gave greater benefit to those with lower vulnerability.

Indexed as

Air PollutionEnvironmental ExposureParticulate MatterDenmarkHumansLow Socioeconomic StatusParisSocioeconomic Disparities in HealthSocioeconomic FactorsParticulate MatterAir pollutionDeprivationGreen infrastructureInequalityPublic healthUrban planning

Identifiers

PMID41068655
PMCPMC12512593

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.