Evidence map›Paper›PMID 29986540›Full record

ArticleSensors (Basel, Switzerland)2018

A Cost-Effective IoT System for Monitoring Indoor Radon Gas Concentration.

Oscar Blanco-Novoa, Tiago M Fernández-Caramés, Paula Fraga-Lamas, Luis Castedo

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
13.1field-weighted citation impact, top 1% 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

21 citing papers in PubMed, 77 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Developing a Secure Low-Cost Radon Monitoring System.Sensors (Basel, Switzerland) · 2020
    Article
  14. Radon-222 Exposure and Dose Concentration Levels in Jordanian Dwellings.Journal of environmental and public health · 2020
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors at 1 institution in 1 country.

Oscar Blanco-NovoaDepartment Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain. o.blanco@udc.es.
Tiago M Fernández-CaramésDepartment Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain. tiago.fernandez@udc.es.ORCID 0000-0003-2179-5917
Paula Fraga-LamasDepartment Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain. paula.fraga@udc.es.ORCID 0000-0002-4991-6808
Luis CastedoDepartment Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain. luis@udc.es.
Universidade da Coruña · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radon is a noble gas originating from the radioactive decay chain of uranium or thorium. Most radon emanates naturally from the soil and from some building materials, so it can be found in many places around the world, in particular in regions with soils containing granite or slate. It is almost impossible for a person to detect radon gas without proper tools, since it is invisible, odorless, tasteless and colorless. The problem is that a correlation has been established between the presence of high radon gas concentrations and the incidence of lung cancer. In fact, the World Health Organization (WHO) has stated that the exposure to radon is the second most common cause of lung cancer after smoking, and it is the primary cause of lung cancer among people who have never smoked. Although there are commercial radon detectors, most of them are either expensive or provide very limited monitoring capabilities. To tackle such an issue, this article presents a cost-effective IoT radon gas remote monitoring system able to obtain accurate concentration measurements. It can also trigger events to prevent dangerous situations and to warn users about them. Moreover, the proposed solution can activate mitigation devices (e.g., forced ventilation) to decrease radon gas concentration. In order to show its performance, the system was evaluated in three different scenarios corresponding to representative buildings in Galicia (Spain), a region where high radon gas concentrations are common due to the composition of the soil. In addition, the influence of using external hardware (i.e., WiFi transceivers and an embedded System-on-Chip (SoC)) next to the radon gas sensor is studied, concluding that, in the tested scenarios, they do not interfere with the measurements.

Indexed as

domoticshome automationIoTradonsensorssmart homewireless sensor networksWSN

Identifiers

PMID29986540
PMCPMC6069144
OpenAlexW2853456495

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.