Biological toxicity of groundwater in a seashore area: Causal analysis and its spatial pollutant pattern

Huang, Wei-Che; Chen, Wei-Yea; Chuang, Yen Hsun; Lin, Yun-Huin; Chen, Ho‐Wen

doi:10.1016/j.chemosphere.2013.12.073

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Water is essential for supporting livelihoods, safeguarding public health, providing food security, ensuring environmental sustainability, promoting industrial and economic development, improving living standards, and achieving sustainable development (Dinka et al, 2015;Falkenmark, 2015;Gwenzi et al, 2015;Huang et al, 2014;Kumar et al, 2015;Mayzelle et al, 2015;Sobowale et al, 2015). It is estimated that about 3.4 billion people in developing countries are highly vulnerable to water insecurity which developed countries have overcomed through massive technological, management investments as reported by Vorosmarty et al (2010) as well as infrastructural investments.…”

Section: Introductionmentioning

confidence: 99%

“…Groundwater quality is affected and determined by anthropogenic and natural processes which include hydrogeological condition of the aquifer, geochemical reactions, lithology, soil-rock water interactions, saline water intrusion, tidal fluctuation, agricultural activities, unplanned rural and urban development, industrialization, land use changes, improper solid and liquid waste disposal and management, groundwater pumping, proximity to and leaching from potential sinks such as poorly designed landfills, septic tanks, and drainage system, and quality of wells and boreholes (Akhtar et al, 2014;Al-ahmadi and El-Fiky, 2009;Huang et al, 2014;Kumar et al, 2015;Oni and Hassan, 2013;Oyelami et al, 2013;Porowska, 2014;Rao, 2014;Srinivas et al, 2013;Vasanthaviger et al, 2013;Wanke et al, 2015). Contaminants in groundwater vary from one location to another and include different kinds of microbes, organic and inorganic substances, anions, cations, heavy metals, and minerals as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of groundwater quality in a rural community in North Central of Nigeria

Sojobi

2016

Environ Monit Assess

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Evaluation of water quality of nine boreholes and three open hand-dug wells in a rural community in North Central Nigeria revealed relative abundance of cations Na > k > Ca > Mg > Zn > Pb and anions Cl(-) > PO4(2-) > SO4(2-) > NO3(-) in the boreholes and cations Ca > Na > K > Mg > Pb and anions NO3(-) > PO4(2-) > SO4(2-) > Cl(-) in the wells. The major contaminants exceeding SON and WHO permissible limits were NO3(-) , Mg, TH, pH and Mg, Pb, TH, pH and DO in the wells and boreholes, respectively. They are attributable to anthropogenic sources such as domestic waste water and poor waste disposal and natural sources such as mineral dissolution from clayey aquifer which made the acidic groundwater unsuitable for consumption unless they are appropriately treated. Correlation studies revealed existence of three major mineral groups in the aquifer Ca-Fe group, Na-Mg group, Zn-K group, as well as a minor group Pb-group, and they determine the chemical composition of the groundwater and the ionic exchange between the groundwater and mineral-bearing clayey aquifer. In order to curb microbial contamination by Enterobacter aerogenes and Escherichia coli, it is recommended that proper latrines and drainages be provided while domesticated animals should be restricted from boreholes and well. Further, treatment with water guard and pur purifier is recommended.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of groundwater quality in a rural community in North Central of Nigeria

Sojobi

2016

Environ Monit Assess

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“…Hierarchical cluster analysis (HCA) is based on a framework of hierarchy to divide or agglomerate the data from different layers repeatedly and generate a dendrogram. This study uses agglomerative hierarchical cluster analysis [56,57] by taking each of the 70 watersheds' monthly NDVI time-series data as an individual cluster in the first stage and combining any two clusters identifying the clusters have the closest distance in between them. This procedure was carried out repeatedly until the number of clusters reaches an optimal number of this study.…”

Section: Statistics Models 241 Hierarchical Cluster Analysismentioning

confidence: 99%

Cluster and Redundancy Analyses of Taiwan Upstream Watersheds Based on Monthly 30 Years AVHRR NDVI3g Data

Tsai

Wong

2021

Atmosphere

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The study uses 30 years of the third generation of Advanced Very-High-Resolution Radiometer (AVHRR) NDVI3g monthly data from 1982 to 2012 to identify the natural clusters and important driving factors of the upstream watersheds in Taiwan through hierarchical cluster analysis (HCA) and redundancy analysis (RDA), respectively. Subsequently, as a result of HCA, six clusters were identified based on the 30 years of monthly NDVI data, delineating unique NDVI characteristics of the upstream watersheds. Additionally, based on the RDA results, environmental factors, including precipitation, temperature, slope, and aspect, can explain approximately 52% of the NDVI variance over the entire time series. Among environmental factors, nine factors were identified significantly through RDA analysis for explaining NDVI variance: average slope, temperature, flat slope, northeast-facing slope, rainfall, east-facing slope, southeast-facing slope, west-facing slope, and northwest-facing slope, which reflect an intimate connection between climatic and orthographic factors with vegetation. Furthermore, the rainfall and temperature represent different variations in all scenarios and seasons. With consideration of the characteristics of the clusters and significant environmental factors, corresponding climate change adaptation strategies are proposed for each cluster under climate change scenarios. Thus, the results provide insight to assess the natural clustering of the upstream watersheds in Taiwan, benefitting future sustainable watershed management.

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“…HCA clustered similar sampling stations (spatial variability), based on four water quality parameters, located along the river stretch. HCA resulted in a dendrogram where linkage distance (rescaled) is represented on y-axis (Huang et al 2014).…”

Section: Hierarchical Cluster Analysis (Hca)mentioning

confidence: 99%

Application of positive matrix factorization to identify potential sources of water quality deterioration of Huaihe River, China

et al. 2019

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Identification of nonpoint source (NPS) pollution is essential for effective water management. In this study we used a combined approach of hierarchal cluster analysis (HCA) and positive matrix factorization (PMF) to identify NPS pollution for Huaihe River basin in China. NH 3-N, COD, DO and pH were regularly monitored weekly over 2 years (2011-2012) from 27 monitoring stations subjected to high anthropogenic and natural changes. As identified by multiple correspondence analyses, the monitoring stations #3, #9 and #21 are located away from the rest of sites. HCA classified all the stations into 4 groups. PMF identified four factors on each group and each season. They were associated with the major causes of Huaihe River water quality deterioration resulted by discharges inputs from urban, agricultural and industrial land uses. Seasonal NPS pollution variation was found, and it is possibly linked with natural processes, for instance hydrological regime. This research work demonstrates the usefulness of PMF model for the identification of NPS pollution in surface waters. Furthermore, our study also shows that urban, agricultural and industrial land uses were the main factors impairing surface water quality, and limiting NPS pollution would be critical for enhancing surface water quality in the study area.

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Biological toxicity of groundwater in a seashore area: Causal analysis and its spatial pollutant pattern

Cited by 12 publications

References 32 publications

Evaluation of groundwater quality in a rural community in North Central of Nigeria

Evaluation of groundwater quality in a rural community in North Central of Nigeria

Cluster and Redundancy Analyses of Taiwan Upstream Watersheds Based on Monthly 30 Years AVHRR NDVI3g Data

Application of positive matrix factorization to identify potential sources of water quality deterioration of Huaihe River, China

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