Evidence map›Paper›PMID 35479187›Full record

ArticleChemical engineering and processing = Genie des procedes = Verfahrenstechnik2022

Optimal waste management during the COVID-19 pandemic.

Aurora Del Carmen Munguía-López, Rogelio Ochoa-Barragán, José María Ponce-Ortega

Abstract read
In one paragraph

Article in Chemical engineering and processing = Genie des procedes = Verfahrenstechnik, 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. Article
  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

3 authors.

Aurora Del Carmen Munguía-LópezChemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Francisco J. Mujica S/N, Ciudad Universitaria, Morelia, Michoacán 58060, México.
Rogelio Ochoa-BarragánChemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Francisco J. Mujica S/N, Ciudad Universitaria, Morelia, Michoacán 58060, México.
José María Ponce-OrtegaChemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Francisco J. Mujica S/N, Ciudad Universitaria, Morelia, Michoacán 58060, México.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There have been many problems generated by the COVID-19 pandemic. One of them is the worrying increase in the generation of medical waste due to the great risk they represent for health. Therefore, this work proposes a mathematical model for optimal solid waste management, proposing a circular value chain where all types of waste are treated in an intensified industrial park. The model selects the processing technologies and their production capacity. The problem was formulated as a mixed-integer linear programming problem to maximize profits and the waste processed, minimizing environmental impact. The proposed strategy is applied to the case study of the city of New York, where the increase in the generation of medical waste has been very significant. To promote recycling, different tax rates are proposed, depending on the amount of waste sent to the landfill. The results are presented on a Pareto curve showing the trade-off between profits and processed waste. We observed that the taxes promote recycling, even of those wastes that are not very convenient to recycle (from an economic point of view), favoring profits, reducing the environmental impact, and the risk to health inherent to the medical waste.

Indexed as

COVID-19Municipal solid waste managementOptimizationProcess intensificationTaxation

Identifiers

PMID35479187
PMCPMC9021047

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.