ArticleEnvironmental geochemistry and health2009
Magneto-biomonitoring of intra-urban spatial variations of particulate matter using tree leaves.
Article in Environmental geochemistry and health, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Innovative Characterization of Particulate Matter Deposited on Urban Vegetation Leaves through the Application of a Chemical Fractionation Procedure.International journal of environmental research and public health · 2020Article
- Particulate matter on foliage of Betula pendula, Quercus robur, and Tilia cordata: deposition and ecophysiology.Environmental science and pollution research international · 2020Article
- Evaluating deciduous tree leaves as biomonitors for ambient particulate matter pollution in Pittsburgh, PA, USA.Environmental monitoring and assessment · 2019Article
- Particulate matter on two Prunus spp. decreases survival and performance of the folivorous beetle Gonioctena quinquepunctata.Environmental science and pollution research international · 2018Article
- Framework for using deciduous tree leaves as biomonitors for intraurban particulate air pollution in exposure assessment.Environmental monitoring and assessment · 2016Review
- Physical characterization, magnetic measurements, REE geochemistry and biomonitoring of dust load accumulated during a protracted winter fog period and their implications.Environmental monitoring and assessment · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preliminary mineral magnetic results from a pilot project investigating the suitability of roadside tree leaves as depositories of vehicular pollution are presented. Tree leaf surfaces (Lime: Tilia europaea; Sycamore: Acer pseudoplatanus) at four roadside and one woodland location in Wolverhampton, UK, have been monitored (July 2003 to November 2003). Mineral magnetic technologies have revealed spatial variations of particulate pollution concentration throughout the conurbation and data analysis indicates that magnetic concentration parameters are suitable proxies for fine particulate pollution, which are particularly hazardous to health. Site-specific traffic management and associated vehicle behaviour appear to be chiefly responsible for the magnetic concentration differences between sites. Magneto-biomonitoring in this way allows the high-resolution spatial mapping of particulate matter (PM) pollution, which may also benefit epidemiology in better assessing exposure to vehicular-derived particulates. Given the speed, measurement sensitivity and non-destructive nature of the technique, it is proposed that this low-cost approach offers some advantages over centralised monitoring stations to monitor urban roadside particulate pollution.
Indexed as
Identifiers
19023666What OpenQuestion holds
Registered trials
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.