Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh

Bodrud-Doza, Md.; Islam, Abu Reza Md. Towfiqul; Ahmed, Fahad; Das, Samiran; Saha, Narottam; Rahman, M. Safiur

doi:10.1016/j.wsj.2016.05.001

Cited by 294 publications

(111 citation statements)

References 42 publications

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“…The scree plot was adopted to detect the number of PCs to be retained to insights into the underlying variable internal structure (Figure 2). The loading scores were demarcated into three groups of weak (0.50-0.30), moderate (0.75-0.50) and strong (> 0.75) respectively (Liu et al, 2003;Bodrud-Doza et al, 2016;Islam et al, 2017). The PC1 (First) elucidated 8.967% of the variance as it encompassed a confidence level of weak positive loading of the healthcare system in Bangladesh (HSB1-3: 0.334-0.459); moderate positively loaded of the healthcare system in Bangladesh (HSB4-10: 0.50-0.746).…”

Section: Overall Relationship Assessment Among the Variablesmentioning

confidence: 99%

<strong>Psychosocial, and Socio-Economic Crisis in Bangladesh due to COVID-19 Pandemic: A Perception-Based Assessment</strong>

Shammi¹,

Bodrud-Doza²,

Islam³

et al. 2020

Preprint

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The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

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Section: Overall Relationship Assessment Among the Variablesmentioning

confidence: 99%

<strong>Psychosocial, and Socio-Economic Crisis in Bangladesh due to COVID-19 Pandemic: A Perception-Based Assessment</strong>

Shammi¹,

Bodrud-Doza²,

Islam³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many researchers have used the DRASTIC [29] or GIS-based DRASTIC models (overlay and indexed method) [30][31][32][33], MOUSE and RUSTIC models (process based simulation methods) [36,37] for water quality assessment while other have applied the clustering analysis (statistical methods) [32,[60][61][62] in which they simply separated different clusters/groups based on similarity or dissimilarity. The clustering analysis is also used in this study, but the interest was to find out the exact threshold values that separate those clusters, and this makes it different than the other studies.…”

Section: Clustering Analysis Of the Groundwater Datamentioning

confidence: 99%

“…A similar high relationship was also found by [66] during the physio-chemical analysis of groundwater in Chithar Riber Basin, India. The high correlation of the EC with chloride and alkalinity also shows that the salinity in the study area's groundwater is controlled by chlorides and bicarbonates [60]. This could probably be derived from weathering of rock salts and gypsum-bearing aquifers [67].…”

Section: Clustering Analysis Of the Groundwater Datamentioning

confidence: 99%

A Simple Approach of Groundwater Quality Analysis, Classification, and Mapping in Peshawar, Pakistan

et al. 2019

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Groundwater is an important source of water for drinking, agriculture, and other household purposes, but high population growth, industrialization, and lack of oversight on environmental policies and implementation have not only degraded the quality but also stressed the quantity of this precious source of water. Many options existed, but this study evaluated, classified, and mapped the quality of groundwater used for potable consumption with a simple approach in an urban area (Peshawar valley) of Pakistan. More than 100 groundwater samples were collected and analyzed for physio-chemical parameters in a laboratory. Hierarchal clustering analysis (HCA) and classification and regression tree (CART) analysis were sequentially applied to produce potential clusters/groups (groundwater quality classes), extract the threshold values of the clusters, classify and map the groundwater quality data into meaningful classes, and identify the most critical parameters in the classification. The HCA produced six distinct potential clusters. We found a high correlation of electrical conductivity with t o t a l h a r d n e s s ( R 2 = 0.72 ), a l k a l i n i t y ( R 2 = 0.59 ) and c h l o r i d e ( R 2 = 0.64 ) , and, t o t a l h a r d n e s s with c h l o r i d e ( R 2 = 0.62), and a l k a l i n i t y ( R 2 = 0.51). The CART analysis conclusively identified the threshold values of the six classes and showed that t o t a l h a r d n e s s was the most critical parameter in the classification. The majority of the groundwater was either with worse quality or good quality, and only a few areas had the worst groundwater quality. This study presents a simple tool for the classification of groundwater quality based on several aesthetic constituents and can assist decision makers develop and support policies and/or regulations to manage groundwater resources.

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“…Trong những năm gần đây, GWQI và PCA được sử dụng rộng rãi trong lĩnh vực môi trường như diễn biến chất lượng nước dưới đất, nước mặt và đánh giá các chỉ thị chất lượng môi trường. Ở Ấn Độ [2,10], Bangladesh [1], Thổ Nhĩ Kỳ [9,11], Ai Cập [3], Nhật Bản [7], các nghiên cứu này đã ứng dụng phương pháp thống kê đa biến (PCA, CA, DA, …) và chỉ số chất lượng nước đánh giá chất lượng nước mặt, nước dưới đất ở các lưu vực sông dựa vào mối quan hệ giữa các thông số đo đạc với các đặc điểm nguồn tầng chứa nước, từ đó đề xuất được các thông số đặc trưng chất lượng nước của lưu vực để giám sát và quản lí hiệu quả. Ở nước ta hiện nay, các kỹ thuật thống kê đa biến (PCA, CA, HCA…) nói chung và phân tích thành phần chính (PCA) nói riêng cũng được ứng dụng rộng rãi, nhưng chủ yếu là các ngành kinh tế và xã hội.…”

Section: Mở đầUunclassified

Application of groundwater quality index (GWQI) and principle component analysis (PCA) to assess the groundwater quality of Pleistocene aquifer in Tan Thanh district, Ba Ria – Vung Tau province

Nguyen¹,

Hoang²,

Pham

et al. 2019

Sci. Tech. Dev. J.-Sci. Earth Environ.

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This research applied the groundwater quality index (GWQI) and principal component analysis (PCA) for assessing pollution levels and factors effecting groundwater quality of Pleistocene aquifer in Tan Thanh district, Ba Ria – Vung Tau province. Seventeen wells were colected in April (dry season) and October (rainy season) during 5 years (2012-2017). Nine parameters of water (pH, TDS, độ cứng, Cl-, F-, NO3-, SO42-, Cu2+ và Fe2+) were collected for analyses. The results of GWQI (dry season) divided into 17 wells in dry season with groundwater quality from good to very good which occupied to 82% databases, compared to 94% of rainy season. Meanwhile, the number of wells significantly changed from poor to very poor water between two seasons. PCA demonstrated that two specific factors, interaction of chemical components in river sediments and character of aquifer and anthropogenic, affected to the quality of groundwater in this study area. These explained 65.555% (dry season) and 61.562% (rainy season) of the variances. This result is provided specific information, and the useful tools in order to deal with the complicated databases, as well as zone the groundwater quality in the study area. This further help the authorities to plan suitable strategy for groundwater quality management.

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Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh

Cited by 294 publications

References 42 publications

<strong>Psychosocial, and Socio-Economic Crisis in Bangladesh due to COVID-19 Pandemic: A Perception-Based Assessment</strong>

<strong>Psychosocial, and Socio-Economic Crisis in Bangladesh due to COVID-19 Pandemic: A Perception-Based Assessment</strong>

A Simple Approach of Groundwater Quality Analysis, Classification, and Mapping in Peshawar, Pakistan

Application of groundwater quality index (GWQI) and principle component analysis (PCA) to assess the groundwater quality of Pleistocene aquifer in Tan Thanh district, Ba Ria – Vung Tau province

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