Evidence map›Paper›PMID 37521456›Full record

ArticleCase studies in chemical and environmental engineering2023

Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning.

Sakine Shekoohiyan, Mobina Hadadian, Mohsen Heidari, Homa Hosseinzadeh-Bandbafha

Abstract readCase Reports
PubMed Full text at PMC Publisher
In one paragraph

Article in Case studies in chemical and environmental engineering, 2023. 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

4 authors.

Sakine ShekoohiyanDepartment of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Mobina HadadianDepartment of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Mohsen HeidariDepartment of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Homa Hosseinzadeh-BandbafhaDepartment of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R

Indexed as

COVID-19Life cycle assessmentMachine learningMunicipal solid waste

Identifiers

PMID37521456
PMCPMC9998284

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.