Groundwater evaluation indices, multivariate statistical techniques, and geostatistical models are applied to assess the source apportionment and spatial variability of groundwater pollutants at the Lakshimpur district of Bangladesh. A total of 70 groundwater samples have been collected from wells (shallow to deep wells, i.e., 10-375 m) from the study area. Groundwater quality index reveals that 50 % of the water samples belong to goodquality water. The degrees of contamination, heavy metal pollution index, and heavy metal evaluation index present diversified results in samples even though they show significant correlations among them. The results of principal component analysis (PCA) show that groundwater quality in the study area mainly has geogenic (weathering and geochemical alteration of source rock) sources followed by anthropogenic source (agrogenic, domestic sewage, etc.). Cluster analysis and correlation matrix also supported the results of PCA. The Gaussian semivariogram models have been tested as the best fit models for most of the water quality indices and PCA components. The results of semivariogram models have shown that most of the variables have weak spatial dependence, indicating agricultural and residential/domestic influences. The spatial distribution maps of water quality parameters have provided a useful and robust visual tool for decision makers toward defining adaptive measures. This study is an implication to show the multiple approaches for quality assessment and spatial variability of groundwater as an effort toward a more effective groundwater quality management.

BackgroundUnderweight is a major cause of global disease burden. It is associated with child mortality and morbidity, and its adverse impact on human performance and child survival is well recognized. Underweight is a major public health problem in Bangladesh, which is amongst the highest underweight prevalent countries in the world. The objectives of our study were to determine the national and regional prevalence rates of underweight and severe underweight in Bangladesh, and to investigate the association of socioeconomic and demographic factors with child underweight and severely underweight among children under the age of five living in Bangladesh.MethodsWe performed a cross sectional study using Multiple Indicators Cluster Survey 2012–13, Bangladesh data on 17,133 children under 5 years of age. Weight-for-age Z scores based upon World Health Organization (WHO) guidelines were used to define child underweight and severe underweight. The association of underweight and severe underweight with household socioeconomic factors and demographic characteristics was investigated using binary logistic regression model.ResultsAn estimated 31.67% children were underweight and 8.81% children were severely underweight. Children of mothers with incomplete secondary education [Odds Ratio (OR) = 0.84, 95% CI: 0.75, 0.94] and mothers with completed secondary education [Odds Ratio (OR) = 0.77, 95% CI: 0.64, 0.93] were less likely to be underweight than children of uneducated mothers who had no formal schooling. A similar association exists for father’s education, children from households in the highest wealth index quintile had lower likelihood of underweight [Odds Ratio (OR) = 0.44, 95% CI: 0.37, 0.53] than children from households in the lowest quintile. Consumption of non-iodized salt had higher risk of severe underweight for children aged between 24 and 35 months [Odds Ratio (OR) = 2.32, 95% CI: 1.80, 3.00]. Other risk factors of child severe underweight included living in Sylhet division and increases in the number of children under the age of five in a household.ConclusionUnderweight was associated with lower parental education, household position in lower wealth index, living in Sylhet division and consumption of non-iodized salt. Strategies are discussed considering the relative importance of risk factors for child underweight.

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This study deals with the natural and anthropogenic processes that influence the surface water quality in the central Bangladesh using multivariate statistical techniques.

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