Evidence map›Paper›PMID 35149107›Full record

ArticleEnvironmental research2022

Modeling spatial variation of gaseous air pollutants and particulate matters in a Metropolitan area using mobile monitoring data.

Jia Xu, Wen Yang, Zhipeng Bai, Renyi Zhang, Jun Zheng, Meng Wang, Tong Zhu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental research, 2022. 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

7 authors.

Jia XuState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States.
Wen YangState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.
Zhipeng BaiState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.
Renyi ZhangDepartment of Atmospheric Sciences, Texas A&M University, Center for Atmospheric Chemistry and the Environment, College Station, TX, United States.
Jun ZhengCollaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China.
Meng WangDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States; Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY, United States; RENEW Institute, University at Buffalo, Buffalo, NY, United States. Electronic address: mwang54@buffalo.edu.
Tong ZhuBIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing, China. Electronic address: tzhu@pku.edu.cn.

Funding

Air Pollution, Coronary Events and Atherosclerotic Progression in a Susceptible PopulationR01ES031986 · NIEHS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI WANG, MENG · 2021 to 2025
$1.6M
NIEHS NIH HHS R01 ES031986
6 · The paper itself

Abstract

Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-m interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R

Indexed as

Air PollutantsAir PollutionOzoneBenzeneEnvironmental MonitoringHumansMethanolParticulate MatterAir PollutantsBenzeneMethanolOzoneParticulate MatterGeo-statistical modelingMobile monitoringSpatial analysisTraffic-related air pollutants

Identifiers

PMID35149107
PMCPMC9203245

What OpenQuestion holds

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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.