Quantitative Assessment for the Effects of Non-point Source Pollution Loads on Water Environment of Plain Area

Abstract

The non-point source (NPS) is the main cause of surface water pollution in Taihu Lake basin in southeast China, therefore the quantitative analysis for the effects of NPS on water environment is the vital basis for water pollution control in the area. Surface water quality models, with the emphasis on the spatial and temporal changes of pollutants, are the most important tools for watershed environment management planning, which provide scientific basis for assessment and control of NPS pollution of watershed environment. However, because of the characteristics of the generation of agricultural NPS pollution loads, its spatial and temporal distribution was averaged in water quality simulating for a long time, which reduced the precision of prediction and the feasibility of real-time forecasting. In water quality simulation, the spatial and temporal distribution of agricultural NPS pollution loads can be determined based on the concept of catchment area of unit river length. When the time scale of calculation is relatively long (e.g. yearly), pollutant load of non-point source can also be averaged annually and put into segments; and when it is relatively short, pollutant load can be distributed based on storm duration. This paper presented the methods of agricultural NPS pollution loads estimation and its spatial and temporal distribution for the plain area watershed. The estimation of NPS pollution load involves the characteristics of runoff pollution loads from different land uses, such as dry land, paddy-field, urban areas etc. In this paper, Suzhou is taken as a case study, for there are so many rivers and streams, which form a complex stream-net. The result of the case study showed that the represented empirical formula for estimation of NPS is feasible. The method of spatial and temporal distribution of NPS pollution loads based on the concept of catchment area of unit river length can improve the prediction precision of water quality models, as well as make it possible for real-time water quality simulation in the plain area.