Temporal and spatial characteristics of surface water quality by an improved universal pollution index in red soil hilly region of South China: a case study in Liuyanghe River watershed

Selecting the Liuyanghe River watershed as an example, using monitoring data of water quality of nearly 10 years and the improved synthesis pollution index method to evaluate the water quality, the research studied the temporal and spatial characteristics of surface water quality of a typical basin in the red soil hilly region, and analyzed reasons for the surface water quality change. The results indicated the improved synthesis pollution index had a better serviceability than other methods, such as, Pollution Index method, Fuzzy Evaluation method, Grey-System method etc. As for the temporal characteristic, because of no-point source pollution, the water quality of Liuyanghe River watershed had become a more and more serious problem over a ten-year period. The spatial characteristic indicated that the pollution degree increased from upstream to downriver. Water quality upstream was better, and the content of the heavy metals was higher in the middle of the river, and the pollution of ammonia nitrogen intensified downriver. The result suggested the improved universal pollution index could be used in the assessment of the water environment.

[1]  R. Hirsch,et al.  Techniques of trend analysis for monthly water quality data , 1982 .

[2]  Nives štambuk-Giljanović,et al.  Water quality evaluation by index in Dalmatia , 1999 .

[3]  T. Ya.  OPINION ON RED DESERTIFICATION AND TYPES OF DESERTIFIED LAND IN CHINA , 2001 .

[4]  N B Chang,et al.  Identification of river water quality using the fuzzy synthetic evaluation approach. , 2001, Journal of environmental management.

[5]  D. Wunderlin,et al.  Reply to comment on "Use of water quality indices to verify the impact of Cordoba city (Argentina) on Suquia River". , 2000, Water research.

[6]  Shang-Lien Lo,et al.  Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. , 2002, Water research.

[7]  L. Hai.  The suggestion of selecting pollution items on water quality evaluation , 2002 .

[8]  S. Chellam,et al.  Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks , 2003 .

[9]  Xu Zu-xin,et al.  Single Factor Water Quality Identification Index for Environmental Quality Assessment of Surface Water , 2005 .

[10]  S. Lo,et al.  A fuzzy index model for trophic status evaluation of reservoir waters. , 2005, Water research.

[11]  Liang Chuan.  Application of fuzzy matter-element model based on coefficients of entropy in comprehensive evaluation of water quality , 2005 .

[12]  F. Ntengwe.  Pollutant loads and water quality in streams of heavily populated and industrialised towns , 2006 .

[13]  P. P. Mujumdar,et al.  Grey fuzzy optimization model for water quality management of a river system , 2006 .

[14]  Jing-nan Sun,et al.  Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. , 2006, Journal of environmental sciences.

[15]  Paulo Chaves,et al.  Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks , 2007 .

[16]  Subhankar Karmakar,et al.  A two-phase grey fuzzy optimization approach for water quality management of a river system , 2007 .

[17]  Zhenli He,et al.  Water quality prediction of marine recreational beaches receiving watershed baseflow and stormwater runoff in southern California, USA. , 2008, Water research.