Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic processes. In this study, classic chemical and multivariate statistical methods were utilized to assess the groundwater quality and ascertain the potential contamination sources affecting the groundwater quality of Galma sub-watershed in a tropical savanna. For this purpose, the data set of 18 groundwater quality variables covering 57 different sampling boreholes (BH) was used. The groundwater samples essentially contained the cations in the following order of dominance: Ca > Na > Mg > K. However, the anions had HCO > Cl > SO > NO respectively. The hydrochemical facies classified the groundwater types of the sub-watershed into mixed Ca-Mg-Cl type of water, which means no cations and anions exceeds 50%. The second dominant water type was Ca-Cl. The Mg-HCO water type was found in BH 9, and Na-Cl water type in BH 29 of the studied area. The weathering of the basement rocks was responsible for the concentrations of these ions in the groundwater chemistry of the sub-watershed. Hierarchical cluster analysis (HCA) grouped the groundwater samples (boreholes) into five clusters that are statistically significant regarding the similarities of groundwater quality characteristics. The principal component analysis (PCA) extracted two major principal components explained around 65% of the variance and suggested the natural and anthropogenic processes especially the agricultural pollutants including synthetic fertilizers, and leaching of agricultural waste as the main factors affecting the groundwater quality. The integrated method proved to be efficient and robust for groundwater quality evaluation, as it guaranteed the precise assessment of groundwater chemistry in the sub-watershed of the tropical savanna. The findings of this investigation could be useful to the policy makers for developing effective groundwater management plans for the groundwater resources and protection of the sub-watershed.
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Source |
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http://dx.doi.org/10.1007/s11356-022-18552-0 | DOI Listing |
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