Journal ArticleDOI
A global Water Quality Index and hot-deck imputation of missing data
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TLDR
The Water Quality Index (WATQI) as discussed by the authors provides a global effort at reporting and estimating water quality on the basis of five commonly reported quality parameters: dissolved oxygen, electrical conductivity, pH value, and total nitrogen and phosphorus concentrations.About:
This article is published in Ecological Indicators.The article was published on 2012-06-01. It has received 174 citations till now. The article focuses on the topics: Water quality & Environmental Performance Index.read more
Citations
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Heavy metal pollution in surface water and sediment: A preliminary assessment of an urban river in a developing country
Saiful Islam,Saiful Islam,Kawser Ahmed,Mohammad Raknuzzaman,Mohammad Raknuzzaman,Habibullah Al Mamun,Habibullah Al Mamun,Muhammad Kamrul Islam +7 more
TL;DR: In this article, the concentration and chemical fractionation of globally alarming six heavy metals (Cr, Ni, Cu, As, Cd and Pb) were measured in surface water and sediment of an urban river in Bangladesh.
Journal ArticleDOI
Global assessment of exposure to faecal contamination through drinking water based on a systematic review
Robert E. S. Bain,Ryan Cronk,Rifat Hossain,Sophie Bonjour,Kyle Onda,James S. Wright,Hong Yang,Tom Slaymaker,Paul R. Hunter,Annette Prüss-Ustün,Jamie Bartram +10 more
TL;DR: To estimate exposure to faecal contamination through drinking water as indicated by levels of Escherichia coli (E. coli) or thermotolerant coliform (TTC) in water sources, seawater samples are analyzed for E. coli andTC levels are counted.
Journal ArticleDOI
Sanitation: A Global Estimate of Sewerage Connections without Treatment and the Resulting Impact on MDG Progress
TL;DR: Redefining sewerage-without-treatment as "unimproved sanitation" in MDG monitoring would raise the 1990 baseline population using unimproved sanitation from 53% to 64% and the corresponding 2015 target from 27% to 32%.
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Measuring global water security towards sustainable development goals
TL;DR: In this paper, a spatial multi-criteria analysis framework is presented to provide a global assessment of water security based on Goal 6 of SDGs, where the term "security" is conceptualized as a function of availability, accessibility, accessibility to services, safety and quality, and management.
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Development of innovative computer software to facilitate the setup and computation of water quality index
Ramin Nabizadeh,Maryam Valadi Amin,Mahmood Alimohammadi,Kazem Naddafi,Amir Hossein Mahvi,Samira Yousefzadeh +5 more
TL;DR: A dataset containing 735 water samples of drinking water quality in different parts of the country was used to show the performance of the IWQIS software, which proved to be an efficient tool to facilitate the setup of water quality indices based on flexible use of variables and water quality databases.
References
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Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Journal ArticleDOI
Missing data: Our view of the state of the art.
Joseph L. Schafer,John W. Graham +1 more
TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Journal ArticleDOI
Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
Journal ArticleDOI
Statistical Analysis with Missing Data
Journal ArticleDOI
Inference and missing data
TL;DR: In this paper, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.