Evidence map›Paper›PMID 32235712›Full record

ArticleInternational journal of environmental research and public health2020

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO

Martin Otto Paul Ramacher, Matthias Karl

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. 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.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The influence of outdoor PMEnvironmental epidemiology (Philadelphia, Pa.) · 2021
    Article
  6. Exposure to long-range transported particulate matter and modeling age-related particle deposition.Environmental science and pollution research international · 2021
    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

2 authors.

Martin Otto Paul RamacherChemistry Transport Modeling, Helmholtz Zentrum Geesthacht, 21502 Geesthacht, Germany.ORCID 0000-0001-5813-2258
Matthias KarlChemistry Transport Modeling, Helmholtz Zentrum Geesthacht, 21502 Geesthacht, Germany.ORCID 0000-0002-0821-018X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO

Indexed as

Air PollutantsAir PollutionEnvironmental ExposureCitiesEnvironmental MonitoringNitrogen DioxideParticulate MatterAir PollutantsNitrogen DioxideParticulate Matterchemistry transport modelingmicroenvironmentsnitrogen dioxidesparticulate matterpopulation exposuretransport emissionsurban air quality

Identifiers

PMID32235712
PMCPMC7142857

What OpenQuestion holds

Textmetadata
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.