Evidence map›Paper›PMID 34061237›Full record

ArticleEnvironmental monitoring and assessment2021

Development of urban air monitoring with high spatial resolution using mobile vehicle sensors.

Kiwon Yeom

Abstract read
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In one paragraph

Article in Environmental monitoring and assessment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.6field-weighted citation impact, top 19% of its field
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

0 citing papers in PubMed, 23 citations in OpenAlex.

No citing paper in PubMed yet.

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

1 author at 1 institution in 1 country.

Kiwon YeomDepartment of Human Intelligence and Robot Engineering, Sangmyung University, Cheonan, South Korea. pragman@naver.com.ORCID http://orcid.org/0000-0002-6867-0305
Sangmyung University · KR

Funding

Sangmyung University 2019
6 · The paper itself

Abstract

Traditionally, the equipment used to measure air pollution is expensive and placed around cities or in mobile laboratories. It might only represent a certain area and not the entire city due to the locations and limited number of monitoring stations. Nowadays, a mobile sensing is becoming an alternative option to monitor air quality in urban environment due to its ease of use, high flexibility, and low price. This paper develops a vehicular-based mobile monitoring system for real time air quality sensing and visualization across large cities with high spatial resolution. The commercially available low-cost CO, NO[Formula: see text], NH[Formula: see text] O[Formula: see text], CH[Formula: see text], SO[Formula: see text], PM[Formula: see text], temperature and humidity sensors along with the microcontroller and GPS were integrated in a sensing device installed on the roof of taxi and sport utility vehicle (SUV). The developed device was calibrated through a reference monitoring station and validated through field measurement. We first split the entire city with a uniform grid discretization. We then propose a data processing methodology based on machine learning algorithms for generating 250 representative data set from 286 million data which is collected using the vehicular based mobile sensors. Next we present the representativeness of the data set by comparison of stationary data and mobile data. We also describe the analytical results and spatial distribution with high spatial resolution throughout the city. In addition, the collected mobile sensor data is also used to show that the significant differences and spatial variability in mean levels per street. Finally, we conclude that the proposed mobile monitoring system using high spatial resolution can effectively map the air quality in metropolitan environment and provide detail about the spatial variability that cannot be done with stationary monitoring systems.

Indexed as

Air PollutantsAir PollutionCitiesEnvironmental MonitoringParticulate MatterAir PollutantsParticulate MatterMobile monitoringSpatial distributionUrban air qualityVehicle sensors

Identifiers

PMID34061237
OpenAlexW3165297634

What OpenQuestion holds

Textmetadata
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.