Evidence map›Paper›PMID 27706087›Full record

ArticleInternational journal of environmental research and public health2016

Characterization of Airborne Particles Collected from Car Engine Air Filters Using SEM and EDX Techniques.

Birmania Heredia Rivera, Martín Gerardo Rodriguez

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

2 authors.

Birmania Heredia RiveraPhysiology and Pharmacology Department, Center of Basic Sciences, Autonomous University of Aguascalientes, Aguascalientes 20931, Mexico. herb62_@hotmail.com.
Martín Gerardo RodriguezPhysiology and Pharmacology Department, Center of Basic Sciences, Autonomous University of Aguascalientes, Aguascalientes 20931, Mexico. mgrodri@correo.uaa.mx.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Particulate matter accumulated on car engine air-filters (CAFs) was examined in order to investigate the potential use of these devices as efficient samplers for collecting street level air that people are exposed to. The morphology, microstructure, and chemical composition of a variety of particles were studied using scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX). The particulate matter accumulated by the CAFs was studied in two categories; the first was of removed particles by friction, and the second consisted of particles retained on the filters. Larger particles with a diameter of 74-10 µm were observed in the first category. In the second one, the detected particles had a diameter between 16 and 0.7 µm. These particles exhibited different morphologies and composition, indicating mostly a soil origin. The elemental composition revealed the presence of three groups: mineral (clay and asphalt), metallic (mainly Fe), and biological particles (vegetal and animal debris). The palynological analysis showed the presence of pollen grains associated with urban plants. These results suggest that CAFs capture a mixture of atmospheric particles, which can be analyzed in order to monitor urban air. Thus, the continuous availability of large numbers of filters and the retroactivity associated to the car routes suggest that these CAFs are very useful for studying the high traffic zones within a city.

Indexed as

Air FiltersAutomobilesAir PollutantsEnvironmental MonitoringMicroscopy, Electron, ScanningParticle SizeParticulate MatterSpectrometry, X-Ray EmissionAir PollutantsParticulate Mattercar air filterparticlepollenSEM microscopy

Identifiers

PMID27706087
PMCPMC5086724

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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.