Evidence map›Paper›PMID 35161750›Full record

ArticleSensors (Basel, Switzerland)2022

An IoT-Aware Solution to Support Governments in Air Pollution Monitoring Based on the Combination of Real-Time Data and Citizen Feedback.

Teodoro Montanaro, Ilaria Sergi, Matteo Basile, Luca Mainetti, Luigi Patrono

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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. Review
  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

5 authors.

Teodoro MontanaroDepartment of Engineering for Innovation, University of Salento, Via Monteroni snc, 73100 Lecce, Italy.ORCID 0000-0003-1750-8268
Ilaria SergiDepartment of Engineering for Innovation, University of Salento, Via Monteroni snc, 73100 Lecce, Italy.ORCID 0000-0002-3797-3039
Matteo BasileDepartment of Engineering for Innovation, University of Salento, Via Monteroni snc, 73100 Lecce, Italy.
Luca MainettiDepartment of Engineering for Innovation, University of Salento, Via Monteroni snc, 73100 Lecce, Italy.ORCID 0000-0001-9387-9277
Luigi PatronoDepartment of Engineering for Innovation, University of Salento, Via Monteroni snc, 73100 Lecce, Italy.ORCID 0000-0002-8591-1190

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the main concerns of the last century is regarding the air pollution and its effects caused on human health. Its impact is particularly evident in cities and urban areas where governments are trying to mitigate its effects. Although different solutions have been already proposed, citizens continue to report bad conditions in the areas in which they live. This paper proposes a solution to support governments in monitoring the city pollution through the combination of user feedbacks/reports and real-time data acquired through dedicated mobile IoT sensors dynamically re-located by government officials to verify the reported conditions of specific areas. The mobile devices leverage on dedicated sensors to monitor the air quality and capture main roads traffic conditions through machine learning techniques. The system exposes a mobile application and a website to support the collection of citizens' reports and show gathered data to both institutions and end-users. A proof-of-concept of the proposed solution has been prototyped in a medium-sized university campus. Both the performance and functional validation have demonstrated the feasibility and the effectiveness of the system and allowed the definition of some lessons learned, as well as future works.

Indexed as

Air PollutionEnvironmental MonitoringCitiesFeedbackGovernmentHumansair pollutiongovernment facilitiesInternet of Thingsmachine learningsmart citiestraffic monitoring

Identifiers

PMID35161750
PMCPMC8839545

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.