Evidence map›Paper›PMID 35190855›Full record

ArticleEnvironmental management2022

Underground Ecosystem Conservation Through High-resolution Air Monitoring.

Rosangela Addesso, Alessandro Bellino, Daniela Baldantoni

Open access · hybridAbstract read
In one paragraph

Article in Environmental management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.8field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 15 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Rosangela AddessoDepartment of Chemistry and Biology "Adolfo Zambelli", University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano (SA), Italy.
Alessandro BellinoDepartment of Chemistry and Biology "Adolfo Zambelli", University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano (SA), Italy. abellino@unisa.it.ORCID 0000-0003-1554-3326
Daniela BaldantoniDepartment of Chemistry and Biology "Adolfo Zambelli", University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano (SA), Italy.
University of Salerno · IT

Funding

Università degli Studi di Salerno ORSA197159
6 · The paper itself

Abstract

In cave ecosystems tourists represent moving sources of discontinuous disturbances, able to induce transient system responses whose knowledge is crucial in defining appropriate conservation measures. Here we propose an approach to evaluate the amplitude and scales of cave alterations based on high-resolution air monitoring, through the use of purposely developed low-cost monitoring stations and a consistent analytical framework for information retrieval based on time series analysis. In particular, monitoring stations adopt a modular structure based on physical computing platforms acquiring data through several sensors, with means of preventing humidity damages and guaranteeing their continuous operation. Data are then analyzed using wavelet periodograms and cross-periodograms to extract the scales of tourism-induced alterations. The approach has been exemplified in the Pertosa-Auletta Cave, one of the most important underground environments in Southern Italy, highlighting the development of monitoring stations and the information obtainable with the proposed analytical workflow. Here, 2 monitoring stations acquiring data for 1 year at 1' sampling time on temperature, relative humidity, CO

Indexed as

EcosystemEnvironmental MonitoringCavesParticulate MatterTemperatureParticulate MatterAtmospheric monitoringFrequency spectraShow cavesTime seriesTourist load

Identifiers

PMID35190855
PMCPMC8860376
OpenAlexW4212950115

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.