Anthropogenic sources of contamination recognition — Sines coastal aquifer (SW Portugal)

Fernandes, Paula Galego; Carreira, Paulà M.; Silva, Manuel Oliveira da

doi:10.1016/j.gexplo.2007.10.003

Cited by 13 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PCA is broadly used in geochemical studies (e.g. Azevedo et al, op cit;Fernandes et al, 2008;Tavares et al, 2008;Cance `s et al, 2008). In the case of a geochemical data matrix, each vector shows that the data spread according to their geochemical affinity and provenance.…”

Section: Introductionmentioning

confidence: 99%

Principal component analysis (PCA) and mineral associations of litoraneous facies of continental shelf carbonates from northeastern Brazil

Marques¹,

Sial²,

Menor³

et al. 2008

Continental Shelf Research

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Principal component analysis (PCA) and mineral associations of litoraneous facies of continental shelf carbonates from northeastern Brazil

Marques¹,

Sial²,

Menor³

et al. 2008

Continental Shelf Research

View full text Add to dashboard Cite

“…The concentration of nitrate ranges between 2.97 and 78.77 and exceeds its permissible limit in the northern and central parts of the study area (Figure ). The increase in nitrate concentrations in the northern and central sections is largely attributed to anthropogenic causes such as chemical fertilizer uses in agricultural fields, animal waste, seepage from the septic tank, crop residues and industrial waste discharge containing nitrogen (Fernandes et al, 2008). The concentration of TDS ranges between 183.92 and 343.93.…”

Section: Resultsmentioning

confidence: 99%

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

Chaudhry

Sachdeva

2021

Water & Environment J

View full text Add to dashboard Cite

This study proposes the evaluation of GIS‐based groundwater quality index (GQI) and spatial variability with a hybrid system that combines Fuzzy Logic with GIS‐based GQI. A case study in the semi‐arid area of Punjab, namely Rupnagar, illustrates the proposed hybrid outline. Two GIS‐based GQI models GQI‐10 (using ten water quality parameters (WQPs)) and GQI‐6 (using six “apprehensive/crucial” WQPs), as well as hybrid Fuzzy Logic GIS‐based GQI (FGQI) (using six crucial WQPs) models, were developed using trapezoidal membership functions and rule sets based on expert's knowledge. Performance comparison of GQI models showed that the FGQI model predicts improved groundwater quality than the GQI‐10 and GQI‐6. FGQI model variance, RMSE, and R2 values are 84.25, 6.49, 0.89, respectively. Results have shown that the developed FGQI model is successful and GQI modelling suggests the use of northern and central groundwater without treatment to be unsafe for drinking purposes.

show abstract

“…The principal components (PCs), which are orthogonal (non-correlated), are in decreasing order of importance (Panda et al 2006). The PCA is the useful tool in identification of a number of reduced latent factors with pollution sources such as spatial (anthropogenic) and temporal (seasonal and climatic) origins of variations that affect quality and hydrochemistry of groundwater (Simeonov et al 2003;Pekey et al 2004;Subba Rao 2006Shrestha and Kazama 2007;Fernandes et al 2008;Koklu et al 2010;Lin et al 2012;Arunprakash et al 2014;Singaraja et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Identification of controlling processes of groundwater quality in a developing urban area using principal component analysis

2015

View full text Add to dashboard Cite

Hydrogeochemistry is an important tool for the evaluation of the effect of human activities on aquifer system. Sixty eight groundwater samples were collected from bore wells during pre-and post-monsoon from Nagpur, a fast growing city in India, to assess the spatial controlling processes of groundwater contamination using principal component analysis (PCA). Groundwater has variable total dissolved solid (TDS) and total hardness (TH) values classifying them from fresh to saline and moderately hard to very hard types. About 36 and 33 % of the total groundwater samples during pre-and post-monsoon, respectively, are not suitable for drinking purpose. The graphical presentation of groundwater chemistry has indicated Ca-HCO 3 , mixed CaNa-HCO 3 and mixed Ca-Mg-Cl types. The PCA summarizes the chemical variables of pH, EC, TDS, TH, TA, Na ? , K ? , Ca 2? , Mg 2? , HCO 3 -, Cl -, SO 4 2-and NO 3 -into two PC loadings, accounting for 61.33 and 62.09 % of the total variance during pre-and post-monsoon, respectively. The first PC shows high loadings of EC, TDS, TH, Cl -, NO 3 -, Ca 2? and Mg 2? , which considered as pollution-controlled processes of anthropogenic sources. Second PC has high loadings of Na ? and HCO 3 -, which is taken as alkalinity and pollution-controlled processes of geogenic and anthropogenic sources, respectively. The PC scores suggest the causes of variation in the groundwater chemistry. Negative values of chloro-alkaline indices suggest the prevalence of reverse ion exchange irrespective of the season, silicate weathering and anthropogenic activities over the controlling of groundwater quality which further PCA. Thus, the PCA helps as a tool to assess the controlling processes of the groundwater quality.

show abstract

Anthropogenic sources of contamination recognition — Sines coastal aquifer (SW Portugal)

Cited by 13 publications

References 10 publications

Principal component analysis (PCA) and mineral associations of litoraneous facies of continental shelf carbonates from northeastern Brazil

Principal component analysis (PCA) and mineral associations of litoraneous facies of continental shelf carbonates from northeastern Brazil

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

Identification of controlling processes of groundwater quality in a developing urban area using principal component analysis

Contact Info

Product

Resources

About