GIS-based DRASTIC model for groundwater vulnerability and pollution risk assessment in the Peshawar District, Pakistan

Abstract

27Groundwater is the most economic natural source of drinking in urban and rural areas which 28 are degraded due to high population growth and increased industrial development. We applied 29 a GIS-based DRASTIC model in a populated urban area of Pakistan (Peshawar) to assess 30 groundwater vulnerability to pollution. Six input parametersdepth to phreatic/groundwater 31 level, groundwater recharge, aquifer material, soil type, slope and hydraulic conductivity - 32were used in the model to generate the groundwater vulnerable zones. Each parameter was 33 divided into different ranges or media types, and ratings = 1-10 were assigned to each factor 34 where 1 represented the very low impact on pollution potential and 10 represented very high 35 impact. Weight multipliers = 1-5 were also used to balance and enhance the importance of 36 each factor. The DRASTIC model scores obtained varied from 47 to 147, which were divided 37 into three different zones: low, moderate and high vulnerability to pollution. The final results 38 indicate that about 31.22%, 39.50%, and 29.27% of the total area are under low, moderate, and 39 high vulnerable zones, respectively. Our method presents a very simple and robust way to 40 assess groundwater vulnerability to pollution and helps the decision makers to select 41 appropriate landfill sites for waste disposals, and manage groundwater pollution problems 42 efficiently. 43 44

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

“…The underground rocks in the adjacent regions (eastern and southern) are limestones, shale, or calcareous quartzite [49]. Depth to groundwater table varies from 5 to more than 50 m from north-east to south-west [33].…”

“…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.…”

“…The thematic map of the final classes of the groundwater quality in the study area ( Figure 8) shows that the worst (#1) and worse (#2) groundwater quality areas (19 union councils) are located in the north, center, south, and south-eastern parts of the study area, and the bad quality (#3) areas are Urmar Miana and Chaghar Matti located at the north and south, respectively. The low groundwater quality at the north and north-eastern parts (worst (#1) and worse (#2) quality areas) are probably due to the fertilizers and pesticides applications in the agricultural fields or domestic and industrial wastewater, which drains into the river in these areas [59] coupled with the lowest groundwater table (less than 1.5 m), sandy soil type, and gravel and sand aquifer material where the contaminants require the shortest travel time to reach the groundwater and move easily from one place to another [33]. Most of the area is under good groundwater quality (#4) (eastern, central, south-eastern areas and some parts of the south and north-east) where the water table is more than 50 m deep [70] and the soil type is silt-loam which helps to restrict or minimize the movement of the contaminants [33].…”

“…The low groundwater quality at the north and north-eastern parts (worst (#1) and worse (#2) quality areas) are probably due to the fertilizers and pesticides applications in the agricultural fields or domestic and industrial wastewater, which drains into the river in these areas [59] coupled with the lowest groundwater table (less than 1.5 m), sandy soil type, and gravel and sand aquifer material where the contaminants require the shortest travel time to reach the groundwater and move easily from one place to another [33]. Most of the area is under good groundwater quality (#4) (eastern, central, south-eastern areas and some parts of the south and north-east) where the water table is more than 50 m deep [70] and the soil type is silt-loam which helps to restrict or minimize the movement of the contaminants [33]. Three union councils such as Hayatabad, Malakandir, and Bazid Khel have better groundwater quality (#5), and only the Shahi Bala union council has the best groundwater quality (#6).…”

“…This categorization can be considered as broad in the union councils such as Sher Kera, Aza Khel, and Shekhan (south-west), and Khatki, Mian Gujjar, Lala, and Jhagra (north-east) where the data was not available, and a detailed water quality analysis will be recommended before planning a water supply scheme. The thematic map produced as a result of the clustering analysis has a similar spatial distribution of the groundwater quality as the final vulnerability map in [33], in which they have assessed the groundwater vulnerability to pollution using a completely different method (GIS-based DRASTIC model). It is also interesting to compare the thematic map of groundwater quality classes (Figure 8) with the spatial distribution maps of different water quality parameters in the study area [12].…”