Evidence map›Paper›PMID 35151745›Full record

ArticleThe Science of the total environment2022

Spatial predictors of heavy metal concentrations in epiphytic moss samples in Seattle, WA.

Michelle C Kondo, Christopher Zuidema, Hector A Moran, Sarah Jovan, Monika Derrien, Weston Brinkley, Anneclaire J De Roos, Loni Philip Tabb

Abstract read
In one paragraph

Article in The Science of the total environment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Michelle C KondoNorthern Research Station, USDA Forest Service, 100 N. 20th St, Ste 205, Philadelphia, PA 19103, United States of America. Electronic address: michelle.c.kondo@usda.gov.
Christopher ZuidemaDepartment of Environmental and Occupational Health Sciences, University of Washington, 4225 Roosevelt Way NE, Seattle, WA 98105, United States of America.
Hector A Moran2130 Aqueduct Avenue, Bronx, NY 10453, United States of America.
Sarah JovanPacific Northwest Research Station, USDA Forest Service, 620 SW Main St. suite 502, Portland, OR 97205, United States of America.
Monika DerrienPacific Northwest Research Station, USDA Forest Service, 400 N 34th St., Suite 201, Seattle, WA 98103, United States of America.
Weston BrinkleyStreet Sounds Ecology, LLC, 312 NW 81st St, Seattle, WA 98117, United States of America.
Anneclaire J De RoosDrexel University Dornsife School of Public Health, Department of Environmental and Occupational Health, 3215 Market Street, Philadelphia, PA 19104, United States of America.
Loni Philip TabbDrexel University Dornsife School of Public Health, Department of Epidemiology and Biostatistics, 3215 Market Street, Philadelphia, PA 19104, United States of America.

Funding

Biostatistics, Epidemiologic & Bioinformatic Training in Environmental HealthT32ES015459 · NIEHS · UNIVERSITY OF WASHINGTON · PI June T. Spector · 2009 to 2026
$10.6M
NIEHS NIH HHS T32 ES015459
6 · The paper itself

Abstract

The use of bio-indicators is an emerging, cost-effective alternative approach to identifying air pollution and assessing the need for additional air monitoring. This community science project explores the use of moss samples as bio-indicators of the distribution of metal air particulates in two residential neighborhoods of the industrial Duwamish Valley located in Seattle, WA (USA). We applied geographically weighted regression to data from 61 youth-collected samples to assess the location-specific area-level spatial predictors of the concentrations of 25 elements with focus on five heavy metals of concern due to health and environmental considerations. Spatial predictors included traffic volume, industrial land uses, major roadways, the airport, dirt roads, the Duwamish River, impervious surfaces, tree canopy cover, and sociodemographics. Traffic volume surrounding sample locations was the most consistent positive predictor of increasing heavy metal concentration. Greater distance from the heavy-industry corridor surrounding the Duwamish River predicted lower concentrations of all metals, with statistically significant associations for chromium and lead in some areas. As the distance from dirt roads increased, the concentration of arsenic and chromium decreased significantly. Percent tree canopy within 200 m of sample locations was a significant protective factor for cadmium concentrations. In addition, percent people of color was significantly positively associated with increasing lead, chromium and nickel concentrations. Our findings underscore the potential influence of heavy industry and mobile sources on heavy metal concentrations, the buffering potential of trees in local environments, and persistent opportunity to improve environmental justice.

Indexed as

Air PollutantsBryophytaMetals, HeavyAdolescentChromiumEnvironmental MonitoringHumansAir PollutantsChromiumMetals, HeavyAir pollutionCommunity science environmental justiceGeographically weighted regressionMoss bio-indicatorsOrthotrichum lyelliiParticulate matter

Identifiers

PMID35151745
PMCPMC12530805

What OpenQuestion holds

Textmetadata
LicenceTDM
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.