TY - JOUR

T1 - A Simple Approach of Groundwater Quality Analysis, Classification, and Mapping in Peshawar, Pakistan

AU - Adnan, Syed

AU - Iqbal, Javed

AU - Maltamo, Matti

AU - Bacha, Muhammad Suleman

AU - Shahab, Asfandyar

AU - Valbuena, Ruben

PY - 2019/12/7

Y1 - 2019/12/7

N2 - 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.

AB - 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.

U2 - 10.3390/environments6120123

DO - 10.3390/environments6120123

M3 - Erthygl

VL - 6

JO - Environments

JF - Environments

SN - 2076-3298

IS - 12

ER -