Assessing the impact of urbanization on groundwater quality of lahore region, Pakistan.

Land use land cover (LULC) dynamics is an important aspect of environmental studies. Lahore is one of the wide-ranging urban cities in the world experiencing rapid development in the form of unplanned urban growth and industrialization, which leads to many adverse consequences. This research focuses on the study of spatio-temporal variability of urbanization and its impact on the water quality index (WQI) in Lahore city using remote sensing (RS) and geographical information systems (GIS). Landsat images (Landsat 7 ETM+, Landsat 8 OLI) between 2005 to 2021 were used to observe the changes in urban growth over seventeen years. GIS is used to create the LULC, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) maps, to study the urbanization impact on the WQI. The results of this study indicate that the groundwater quality of metropolitan Lahore city has significantly dropped within 17 years. The extent of the built-up area has been expanded from 22.4% to 953.04% with an increase in the poor WQI area from 1.95% to 37.89%, reveals a general decline in groundwater quality with urbanization. Indeed, the trends observed by the linear regression modelling showed a positive and negative correlation (R2 = 0.67 and -0.74) of WQI with % of urban and vegetation areas respectively. GIS and RS tools have been found effective in assessing spatio-temporal phenomena of urbanization and its impact on groundwater quality. Furthermore, this research would be very helpful in making decisions for managing groundwater resources and illegal urban expansion in Lahore city.

Hydrogeochemical characterization of the groundwater of Lahore region using supervised machine learning technique.

The cationic and anionic composition in groundwater can be better understood by identifying the type of hydrogeochemical processes influencing groundwater chemistry. This research deals with the characterization of groundwater samples by considering the likely role of hydrogeochemical processes and the factors responsible for the weathering process. The study applies statistical methods and supervised machine learning algorithm (i.e., logistic regression model) on the large data set of 1300 water samples from the Lahore district of Punjab, Pakistan. All the water samples were collected by the local authorities from a deep unconfined aquifer (> 350 ft in depth) for the years of 2005 to 2016. The characterization of groundwater quality parameters includes pH, total dissolved solids (TDS), electrical conductivity (EC), total hardness (TH), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), chloride (Cl-), bicarbonate (HCO3-), nitrate (NO3-), and sulfate (SO42-). The results show the sequence of the major ion in the following order: Na+ > Ca2+ > Mg+ > K+ and HCO32- > SO42- > Cl- > NO3-. The ionic ratios and Gibb's plot revealed that the prominent hydrogeochemical facies of aquifer water is Ca-HCO3, Ca-Na-HCO3, and mixed Ca-Mg-Cl type rock-weathering process, especially carbonate and silicate weathering, as significant process controlling water chemistry. The statistical evaluation of the prepared regression model determined its prediction accuracy as 92.2%, which means the model is highly efficient and satisfies the analysis. The outcomes of this study favor the utilization of such methods for other areas with large data sets.