Evidence map›Paper›PMID 34451012›Full record

ArticleSensors (Basel, Switzerland)2021

Using an Internet of Behaviours to Study How Air Pollution Can Affect People's Activities of Daily Living: A Case Study of Beijing, China.

Guangyuan Zhang, Stefan Poslad, Xiaoping Rui, Guangxia Yu, Yonglei Fan, Xianfeng Song, Runkui Li

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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
3.1field-weighted citation impact, top 9% 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

2 citing papers in PubMed, 19 citations in OpenAlex.

  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 at 4 institutions in 2 countries.

Guangyuan ZhangIoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.ORCID 0000-0002-5028-2291
Stefan PosladIoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.ORCID 0000-0002-3156-9609
Xiaoping RuiSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China.ORCID 0000-0002-7764-4272
Guangxia YuIoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
Yonglei FanIoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
Xianfeng SongCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.ORCID 0000-0001-6871-8199
Runkui LiCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Queen Mary University of London · GBChinese Academy of Sciences · CNHohai University · CNUniversity of Chinese Academy of Sciences · CN

Funding

Beijing Municipal Natural Science Foundation 8172046China Scholarship Council NoneFundamental Research Funds for the Central Universities 2019B02514National Key Research and Development Program of China 2017YFB0503605National Natural Science Foundation of China 41771478Queen Mary University of London None
6 · The paper itself

Abstract

This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people's activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples' density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people's desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens' activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens' activities of daily living.

Indexed as

Air PollutantsAir PollutionActivities of Daily LivingBeijingChinaEnvironmental MonitoringHumansInternetParticulate MatterAir PollutantsParticulate Matterair pollutionair quality index (AQI)internet of behavioursinternet of thingspeople’s activities of daily livingPM2.5

Identifiers

PMID34451012
PMCPMC8402293
OpenAlexW3193586460

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.