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Spatial patterns of pollutants in water of metropolitan drain in Lahore, Pakistan, using multivariate statistical techniques

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Abstract

The present study was conducted to investigate the pollutant load in surface water of the Sattukatla drain in Lahore, Pakistan. This drain is receiving high volumes of pollutants from industries and municipal sewage. To study the pollution status of this urban drain, different parameters such as pH, EC, TDS, BOD, NO3, PO4, COD, K, Mg, Ca, Ni, Fe, Co, Pb, Cu, Cd, and Zn were analyzed. These 17 parameters were monitored along the longitudinal gradient of drain consisting of 15 key sampling sites. The studied parameters were subjected to hierarchical cluster analysis (HCA) and factor analysis (FA). The HCA divided 15 sampling sites into three zones, i.e., low pollution, medium pollution, and high pollution. The PCA identified three major contributors, industrial followed by municipal sewage and agricultural sources. The trend of heavy metal levels was observed as Cu > Zn > Fe > Pb > Co > Ni > Cd. The results indicate levels of pollutants above the permissible limits described by NEQS, Pakistan for wastewater. There is an urgent need to address the issue and install treatment plants to reduce the pollution load on this urban drain in Lahore city.

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Acknowledgements

The first author would like to acknowledge the Higher Education Commission (HEC), Pakistan, for providing a facility in the Environmental Science Laboratory, Kinnaird College for Women Lahore, Pakistan, for drain water sample analysis. Special thanks to Dr. Rashid Mahmood, Mr. Sana Ullah, Mr. Luckman Ahmed, and Mr. Amir Sial for their continued efforts during the sampling period. The first author also appreciates the kind provision rendered by Professor Dr. Rukhsana David during the sample analysis in Kinnaird College for Women Lahore, Pakistan.

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Correspondence to Shumaila Majeed.

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Majeed, S., Rashid, S., Qadir, A. et al. Spatial patterns of pollutants in water of metropolitan drain in Lahore, Pakistan, using multivariate statistical techniques. Environ Monit Assess 190, 128 (2018). https://doi.org/10.1007/s10661-018-6504-y

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