Appraisal of river water quality using multivariate statistics and water quality indices: a case of Hindon River (India)

A good quality water is essential for all communities living above and below the land surface. However, anthropogenic activities continuously deteriorate surface water quality (WQ) in many parts of the world. This study was conducted to assess Hindon River WQ using multivariate statistical tools and water quality indices in Uttar Pradesh (India). Water samples were collected and analyzed for 20 hydrological parameters and eight heavy metals in pre- and post-monsoon seasons. Water quality index (WQI) and comprehensive pollution index (CPI) were computed to assess water pollution load. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to identify the major influencing variable and pollution source apportionment. Maximum range was recorded for electrical conductivity (EC; 821.0-2242.6 mu s/cm), total dissolved solids (TDS; 303.0-990.5 mg/l), total hardness (TH; 159.0-320.6 mg/l), total alkalinity (TA; 199.0-519.0 mg/l), biochemical oxygen demand (BOD; 20.4-94.6 mg/l), and chemical oxygen demand (COD; 58.8-292.4 mg/l) in pre-monsoon. The river carried considerable amounts of organic and inorganic pollutants discharged from sugar and pulp industries and municipal wastes. Value of WQI fell under unfit for human consumption as well as CPI showed moderately to severally polluted WQ. PCA shows that TA, TDS, nitrate (NO3-), EC, potassium (K+), turbidity (Turb), fluoride (F-), COD, calcium (Ca2+), magnesium (Mg2+), BOD, bicarbonate (HCO3-), TH, and pH are the major influential variables. In addition, HCA represents four clusters in pre-monsoon and three clusters in post-monsoon that represent slightly to severely polluted sites. The present study shows that the mid-stretch of Hindon River WQ is deteriorated due to numerous anthropogenic activities. Thus, corrective measures are needed by the concerned authorities for improvement of WQ as well as to restore the riverine ecosystem. This study also highlighted the importance of multivariate statistics and water quality indices in assessing river WQ and pollution source apportionment.