Evidence map›Paper›PMID 37711364›Full record

ArticleGeoHealth2023

Data-Driven Placement of PM

Makoto M Kelp, Timothy C Fargiano, Samuel Lin, Tianjia Liu, Jay R Turner, J Nathan Kutz, Loretta J Mickley

Abstract read
In one paragraph

Article in GeoHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Improved daily PMScience advances · 2025
    Article
  6. Article
  7. 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

7 authors.

Makoto M KelpDepartment of Earth and Planetary Sciences Harvard University Cambridge MA USA.ORCID https://orcid.org/0000-0003-0451-3439
Timothy C FargianoCenter for the Environment Harvard University Cambridge MA USA.
Samuel LinDepartment of Computer Science Harvard University Cambridge MA USA.
Tianjia LiuDepartment of Earth System Science University of California, Irvine Irvine CA USA.
Jay R TurnerDepartment of Energy Environmental and Chemical Engineering Washington University St. Louis MO USA.ORCID https://orcid.org/0000-0001-7787-6244
J Nathan KutzDepartment of Applied Mathematics University of Washington Seattle WA USA.
Loretta J MickleyJohn A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA USA.ORCID https://orcid.org/0000-0002-7859-3470

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM

Indexed as

citizen scienceenvironmental justicefine particulate matter (PM2.5)sensor networkssensor placement

Identifiers

PMID37711364
PMCPMC10499371

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.