Evidence map›Paper›PMID 22773080›Full record

ArticleEnvironmental monitoring and assessment2013

Assessment of anthropogenic influences on surface water quality in urban estuary, northern New Jersey: multivariate approach.

Jin Y Shin, Francisco Artigas, Christine Hobble, Yung-Seop Lee

Abstract read
PubMed Publisher
In one paragraph

Article in Environmental monitoring and assessment, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  7. Article
  8. Status of metal levels and their potential sources of contamination in Southeast Asian rivers.Environmental science and pollution research international · 2014
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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.

Jin Y ShinNew Jersey Meadowlands Commission, Meadowlands Environmental Research Institute, 1 DeKorte Park Plaza, Lyndhurst, NJ 07071, USA. jinyoung.shin@njmeadowlands.gov
Francisco Artigas
Christine Hobble
Yung-Seop Lee

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Concentrations of selected heavy metals (Cd, Cr, Cu, Pb, Ni, Fe, and Zn), nutrients (NO (3) (-) and NH(3)), fecal coliform colonies, and other multiple physical-chemical parameters were measured seasonally from 12 locations in an urban New Jersey estuary between 1994 and 2008. Stepwise regression, principal component analysis, and cluster analysis were used to group water quality results and sampling locations, as well as to assess these data's relationship to sewage treatment effluents and the distance to the mouth of the river. The BOD(5), NH(3), NO (3) (-) and fecal coliform counts clustered as one group and positively correlated to the distances from treated effluent and the measures of magnitude at the discharge points. Dissolved solids and most metal species scored high along a single principal component axes and were significantly correlated with the proximity to the industrialized area. From these data, one can conclude that the effluent discharge has been a main source of anthropogenic input to the Hackensack River over the past 15 years. Therefore, the greatest improvement to water quality would come from eliminating the few remaining combined sewer overflows and improving the removal of nutrients from treated effluents before they are discharged into the creeks and river.

Indexed as

Environmental MonitoringEstuariesCitiesFresh WaterHumansHydrogen-Ion ConcentrationMetalsMultivariate AnalysisNew JerseySalinityUrbanizationWater MicrobiologyWater Pollutants, ChemicalWater Pollution, ChemicalMetalsWater Pollutants, Chemical

Identifiers

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.