Evidence map›Paper›PMID 35091932›Full record

ArticleEnvironmental science and pollution research international2022

Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling.

Asma El Amri, Soumaia M'nassri, Nessrine Nasri, Hanen Nsir, Rajouene Majdoub

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Asma El AmriLaboratory of Research in Management and Control of Animal and Environmental Resources in Semi-aride Ecosystem, Higher Agronomic Institute of Chott Meriem, University of Sousse, BP 42, 4042, Chott Meriem, Sousse, Tunisia.
Soumaia M'nassriLaboratory of Research in Management and Control of Animal and Environmental Resources in Semi-aride Ecosystem, Higher Agronomic Institute of Chott Meriem, University of Sousse, BP 42, 4042, Chott Meriem, Sousse, Tunisia. mnassrisoumaia@gmail.com.
Nessrine NasriHigher Institute of Environmental Technologies, Urban Planning and Construction, University of Carthage, 2035, Charguia II, Tunis, Tunisia.
Hanen NsirLaboratory of Research in Management and Control of Animal and Environmental Resources in Semi-aride Ecosystem, Higher Agronomic Institute of Chott Meriem, University of Sousse, BP 42, 4042, Chott Meriem, Sousse, Tunisia.
Rajouene MajdoubLaboratory of Research in Management and Control of Animal and Environmental Resources in Semi-aride Ecosystem, Higher Agronomic Institute of Chott Meriem, University of Sousse, BP 42, 4042, Chott Meriem, Sousse, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Agricultural activities have become a major source of groundwater nitrate contamination. In this context, this study aims to analyse nitrate concentrations in a shallow aquifer of Mahdia-Kssour Essef in central-eastern Tunisia, identify the assignable sources, and predict the future levels using artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models. The results showed that nitrate concentrations measured in 21 pumping wells across the plain ranged from 17 to 521 mg L

Indexed as

GroundwaterWater Pollutants, ChemicalEnvironmental MonitoringNeural Networks, ComputerNitratesNitrogen OxidesTime FactorsTunisiaNitratesNitrogen OxidesWater Pollutants, ChemicalARIMAArtificial neural networkGroundwater qualityNitrate pollutionTime seriesTunisia

Identifiers

PMID35091932

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.