Evidence map›Paper›PMID 31181686›Full record

ArticleInternational journal of environmental research and public health2019

Analyzing Municipal Solid Waste Treatment Scenarios in Rapidly Urbanizing Cities in Developing Countries: The Case of Dar es Salaam, Tanzania.

Emmanuel Kazuva, Jiquan Zhang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. 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. Review
  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

2 authors.

Emmanuel KazuvaSchool of Environment, Northeast Normal University, Jilin Province, Changchun 130024, China. yim124@nenu.edu.cn.
Jiquan ZhangSchool of Environment, Northeast Normal University, Jilin Province, Changchun 130024, China. zhangjq022@nenu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, large quantities of municipal solid waste (MSW) in many cities of the developing countries are being dumped in informal or formal but unregulated dumpsites that threaten the ecological environment and general public health. The situation in Dar es Salaam, Tanzania is of particular concern and is further challenged by a rapidly growing population and urbanization without adequate waste management systems. Current MSW treatment options have been selected based on the judgment and the experience of individuals with authority while underestimating the role of scientifically derived techniques. This study analyzes the most efficient waste treatment options, particularly scenarios with the lowest economic and environmental costs (EcC and EnC, respectively). It uses 12 years (2006-2017) of MSW management data and compares potential waste treatment options for the identified waste streams. A total of 108 different scenarios were designed, and a multi-criteria analysis method was applied to enable the identification of 11 scenarios with acceptable EcCs and EnCs. These formed an initial decision matrix of aggregation dominance that was then categorized into four groups, each represented by the most ideal point. Finally, the dominant scenario that formed the core for all considered options was found. It costs around $274,100 USD while saving about 1585 metric tons (MT) of CO

Indexed as

Developing CountriesSolid WasteUrbanizationCitiesHumansRecyclingTanzaniaWaste Disposal FacilitiesWaste ManagementSolid WasteDar es Salaameconomic costELECTREenvironmental costmulti-criteria analysismunicipal solid wastescenariostreatment options

Identifiers

PMID31181686
PMCPMC6604025

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Registered trials

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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.