Evidence map›Paper›PMID 27817144›Full record

ArticleEnvironmental science and pollution research international2017

Assessment of arsenic, fluoride, bacteria, and other contaminants in drinking water sources for rural communities of Kasur and other districts in Punjab, Pakistan.

Nasima Arshad, Saiqa Imran

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Article in Environmental science and pollution research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.9field-weighted citation impact, top 16% 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

5 citing papers in PubMed, 41 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Nasima ArshadDepartment of Chemistry, Allama Iqbal Open University, Islamabad, Pakistan. nasimaa2006@yahoo.com.
Saiqa ImranDepartment of Chemistry, Allama Iqbal Open University, Islamabad, Pakistan.
Allama Iqbal Open University · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High levels of arsenic contamination in drinking water of two villages, Badarpur and Ibrahimabad of district Kasur, central Punjab, Pakistan is reported first time in present studies. Groundwater quality situation was found to be impaired when samples of different rural areas of district Kasur were monitored according to Pakistan Standards and Quality Control Authority (PSQCA) for all significant water quality constituents and analyzed for trace elements, physico-chemical, and microbiological parameters. Out of 35water sources, 97 % were found unsafe and only 3 % of the sources were within safe limits. High concentrations of arsenic, fluoride, and bacteria were found in 91, 74, and 77 % sources of drinking water, respectively. Very high concentrations of arsenic ranging 58-3800 μg/L were found in the water samples obtained from Badarpur and Ibrahimabad. A decrease in water contamination was observed with increase in source depth. The health issues like arsenicosis and skeletal/dental flourosis were observed in the residents of the monitored areas. Drinking water quality conditions of some rural areas of northen and southern districts of Punjab was also analyzed and compared with Kasur district. High levels of nitrates were found in the samples of Islamabad and Rawalpindi, while high levels of arsenic, iron, fluoride, and TDS were found in Bahawalpur district. Graphical abstract ᅟ.

Indexed as

Water MicrobiologyArsenicBacteriaDrinking WaterFluoridesHumansIronNitratesPakistanQuality ControlRural PopulationTrace ElementsWater Pollutants, ChemicalWater QualityArsenicDrinking WaterFluoridesIronNitratesTrace ElementsWater Pollutants, ChemicalArsenic, fluoride and bacterial pollutionDistrict Kasur, PakistanGround water monitoringHealth hazardsRural areas

Identifiers

PMID27817144
OpenAlexW2546713263

What OpenQuestion holds

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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.